360 Matching Annotations
  1. Feb 2019
    1. Network centralization

      degree.cent <- centr_degree(g, mode = "all") degree.cent$res degree.cent$centralization degree.cent$theoretical_max

  2. Jan 2019
    1. Now, you could ask questions such as, how does being on the outside (periphery) or inside (center) of a friendship network correlate with one's grade point average? Or, do birds of a feather flock together? That is, are students with similar grade point averages likely to nominate each other?

      What variables are more important in the determination or change of friendship in different stages, grades, gender, interest, or similar economic background or experience? What self-identity cognition would be reflected from it?

    2. While you might assume that being on the periphery of a network is disadvantageous, often these peripheral members have ties to other people within or external to the network in which they may occupy important positions. In these instances, the actor serves as bridge to other groups or networks.

      Interesting. The unlimited extension of connectionism in the social network.

    3. strength

      what constitute a tie? direction, same-sex, other-sex, specific subgroups, strength(very subjective to define the strength of a friendship).

    4. computational models

      A computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation.

    5. a group

      the characteristics and relation in subgroups/sub-clustering, and the relation or information transmission among subgroups.

    6. If this is the way that people get clustered, how do we step out and reach more?

    1. To sign up for our private Slack team, go to: https://snaed.slack.com/signup

      When I go to the link to sign up for Slack, I get an error message saying "This team's administrator has not enabled email signups. Ask your administrator to send you an invitation."

  3. Apr 2017
    1. Appendix. Technical demonstration of the SOMprocedure

      This is a great example of Kohonen's Self-organizing Maps and the use of the U-Matrix. The authors were very thorough in explaining how it can be used.

    2. The SOM consisted of 360 neurons on a 24by 15 map grid, with hexagonal lattice andGaussian neighborhood function.

      Data Structure

    3. (Kohonen’s Self-Organizing Maps

      This is the novel innovation approach to network analysis that I will be discussing in my article review presentation

    1. Ryu, S., & Lombardi, D. (2015). Coding Classroom Interactions for Collective and Individual Engagement. Educational Psychologist, 50(1), 70–83. http://doi.org/10.1080/00461520.2014.1001891 (Note: An attempt to combine SNA with critical discourse analysis.)

      I really like this concept and application of SNA with integration of critical discourse analysis (CDA), which is a course I am taking in the fall!! In another class, we read about critical quantitative approach, and I now think what makes it truly critical might be the integration of a mixed method approach, where something like ethnographic case studies or in this case discourse analysis is used in combination with SNA/statistical analyses to give it more robust findings.

    1. address the questions in the previous paragraph

      These are important questions that need answered to truly understand complex social interactions.

    2. ERGMs are the primary building blocks of statistically testing network structural effects. Increasingly, researchers are not only interested in describing an ego or complete network but rather in whether an observed network property is significant. ERGMs [Page 179]generate (random) networks derived from features of the observed network, which provide a way to compare the observed and simulated networks. Statistical analysis is then conducted to test whether the ties in the simulated network match those generated by the simulations.

      They provide a base for comparison similar to a control group?

    3. networks change, and in some instances quickly

      this is something i've been struggling with this entire course. in particular, for my network, one or two members who are always participating and holding these networks together could very easily get a new job, move onto a new opportunity etc. how to keep the networks so intertwined that one or two people leaving wouldn't make the whole thing fall apart is what interests me.

    4. artifacts, direct observation, laboratory experiments, and documents as data sources, and usually there are no plausible ways of identifying populations and drawing samples

      which is sort of amazing to me, because this is exactly what I think a qualitative approach like ethnographic case studies rely on as well!!!

    5. simulations permit inferences and hypothesis testing using network data that are by definition nonindependent observations.

      This sounds like the answer to a question - wait, there's the question in the follow up paragraph below: why are simulations necessary...? :)

    6. dependencies between a network's actors. For example, associations among network exposure (e.g., attitudes of one's peers), network indicators (e.g., size, transitivity), and individual attributes are nonindependent

      This seems key - especially when I think of how we study covariates in statistics and how predictor variables can be confounding, mediating or moderating...

    7. The average of the simulated distribution of reciprocal ties is calculated and then compared to the value in the empirical (observed) network

      Can ERGMs be used for nonreciprocal networks?

    8. except, of course, ego-level network studies from which egos have been randomly drawn from some target population)

      I am using ego-level networks for my final project, but how would I go about ensuring that the ego in question was chosen randomly?

    9. artifacts

      Maybe I missed something in our previous readings/videos, but can someone explain to me what is meant by the term "artifacts"?

    10. Even the tools of predictive modeling are commonly applied to network data (e.g. correlation and regression)

      Would running such tests require a need for assessing latent variables that emerge from network analysis? I will keep reading, but from what I know of SNA, it seems like you are only analyzing observable variables and it would be difficult to obtain a correlation from such unique variables. Am I way off here?

    11. factor analysis,

      I would actually be interested in conducting a factor analysis on my data. Though I am a bit confused about how such a test would be useful for SNA

    12. the sample in these instances is the same as the population.

      For my current data set, this is true. However, it would be nice to be able to make generalizations to a larger population.

    13. limiting the types of questions that could be asked,

      I have been dealing with this issue all semester. I am really struggling to determine which questions are appropriate and which can be answered using SNA techniques. Perhaps this is due to the type of data I am working with, but I am still working on developing sound research questions.

    14. focusing on a conceptual understandin

      I am really looking forward to better understanding the concepts of SNA... beyond the explanation of just looking at relationships.

    15. While these approaches are varied and perhaps even complicated, the key is that they are predicated on the idea of comparing an observed network property to a whole bunch of simulated networks.

      a good conclusion, and I will keep this in mind while going through next chapter.

    1. analyses using these ego-level measures can be done using statistical packages like SPSS

      I am familiar with SPSS, but I had not considered using it for my final project until now. I am a little confused about how I would have to code the data (adjacency matrix) so that it works well in SPSS, but I think I'm going to try it just to see if I can get the same results that I obtain from using R.

    2. UCINET is NetDraw, a visualization tool that has advanced graphic properties.

      I am interested in learning more about the NetDraw software in UCINET. While I like statistics, nothing really says SNA like having clear visuals that display network ties

    3. visualizing your network in the form of a graph provides some hints as to whether your motivating research questions or hypotheses are worth pursuing or in need of revision.Figure

      I need to more practice with creating cleaner graphs that show my network. So far, I've been sticking with the orange and blue colors but I plan to get a little more creative for the final project

    4. Now that your network's boundary has been specified, you are ready to collect your data.

      My project deviated from the author's process because I actually obtained the dataset before identifying research questions. I am using a public dataset so I had to develop me project around what I had available

    5. First, start with an initial topic of interest and then turn this topic into a question.

      Throughout the semester, I have struggled with turning my research topic into a researchable question. I think I've come up with some decent questions, but I'm constantly rewording them.

    6. Where do [Page 270]you set the limits when collecting complete network data when, in theory, there are no limits (Barnes, 1979; Knoke & Yang, 2008)?

      Due to classroom size, I am very limited here with what I could use for complete networks and I'm concerned with the small sample size that the combined quantitative analysis will not show statistical significance

    7. y another set of variables

      I have to develop this more in my project and see how other variables are affecting my network and the results.

    8. While on the one hand, this seems straightforward, on the other hand, such a study can lead to a dizzying amount of complexity.

      The more amount of data that I add in for quantitative analysis has made the interpretation more difficult

    9. methodological transactionalism

      This paradigm shift may be supported by a similar shift from individualized learning theories to socio-cultural theorning theories too.

    10. binary

      I have a lot of node attribue data that is binary (workday track), but I also have a lot of atttribute data that I force to be binary. I think it works, and I have reasons for each fit, but ultimately it simply eases the analysis.

    11. you will determine whom you plan to study

      To what degree can this be an iterative process? After I saw the network data that I collected, I became interested in interviewing a particular subset of students. Though I may have been able to predict the intrigue that the friendship network created, I didn't. Only after seeing the data, did I develop more finely tuned problems to study.

    12. Research questions or hypotheses flow directly from your preferred theoretical framework—assuming, of course, you are operating in a deductive manner

      To what degree may questions develop after data has been collected? Depending on the type of data collected, these new quesitons may already have relevant data to support conclusions.

    13. it is critical that you immerse yourself in the relevant literature that has developed and tested various components related to these and other network-based theories.

      A question I have had for awhile relates to the timeframe for developing a theoretical framework, and how to present that experience in a manuscript. It seems to be a good idea to understand a theoretical framework well after consulting the literature before collecting data. Often enough, I suppose data will not match the theoretical framework, which may force an adjustment of somesort. How do you present this in a manuscript? This experience may lead to the richest conclusions, but I could see authors writing to different audiences and varying their emphasis on the theoretical shift.

    14. Table 12.1

      Consult literature that is not solely focused on education. That'll be important for me to always remember.

    15. social capital

      Sounds interesting! I'd be curious to learn more about this.

    16. patterns of missingness

      I need to look more into how to address the absence of certain data points. If I recall correctly, 133 of 144 students in the 9th grade at my school participated. Reasons for absences vary, and I need to look into what to do about the missing nodes. Any suggestions?

    17. it also supports a larger number of statistical procedures

      This sounds powerful, I will rethink my choice, before make a decision I may need to get to know more about UCINET and NetMiner, make a comparison.

    18. UCINET

      I may prefer to try this package later since my two friends used this software for years, I think I could get more help when I am stuck.

    19. whether even inferential results are generalizable to other populations

      a huge limitation to the application of SNA in general, in my opinion. while it's fascinating to look at particular networks and see some other structures in some of the studies i was reading to better understand my own issues, the findings weren't something i would be comfortable applying to my situation. people are so unique and add a social network of other individuals on top of that, plus environment, plus any number of other factors and it feels very limiting.

    20. as peers both seek out other “deviants” as well as influence each other's behavior

      can confirm. was in high school once.

    21. isn't too large and dense

      yeah, this wasn't so useful for an interconnected network of over 400 individuals.....

    22. the key in this process is to think about how a network variable (either relational or structural, Chapter 3), relates to, affects, or is affected by another set of variables.

      This is related to my project, exploring how the structural and positions of coauthor-ship network affect scholars' academic performance.

    23. You will be somewhat surprised how expanding your search in these ways will yield a bigger, better, and deeper pool of literature that can be used to inform your research questions and design

      Since no one had "done" what I was looking to do I also found it helpful to look for studies that had the same general goal as mine. Even if we were talking about departments in a huge corporation or different professional development groups and how they shared knowledge it was pretty easy to find things in common with my own information.

    24. Nine-Step Process for Conducting a Social Network Analysis. Adapted from Prell (2012)

      This list of words as an image and not words is making the accessibility part of my brain twitch. I'm sure this is just a formatting thing that the automated system that makes this available online did but... what about the screen readers?!

    25. ONA can either be (1) person centric, consisting of a number of questions about each respondent, or (2) question centric to evaluate the relationship for each question

      This is a useful way to think about questions and especially when designing surveys in terms of desired objectives.

    26. analyses presented throughout this book

      I would be interested in trying this especially because this textbook provides some good step-by-step examples using this package.

    27. relationships are key antecedents and determinants of individual behaviors and attitudes

      I just like the phrasing here. antecednets and determinants - strong, but true.

    28. reducing to a number—the complexity of interpersonal relations

      yes, but at least they are accounted for in some way. Traditionally, these are either ignored or mentioned as something that will be left out because it is too complex.

    29. networks’ influences on students’ achievement, attitudes toward school, and degree attainment

      reciprocal relationships are very interesting and relevant to my research interests.

    30. intuitive feel for the network's topography.

      I really like how this chapter is written and how they take us through all the steps. Love the idea that looking at many networks will lead to an "intuitive feel" #snagoals :)

    31. hypothetical example of a questionnaire

      I would love to see more of these - in published papers, for example - or just tutorials that take us from raw data to tidy data to analysis/computations in R.

    32. Likert-type scales

      I hope to use survey design as a component of my data collection for a future project.

    33. observations, interviews

      I hope this will play a big role in my future projects

    34. calculate a few of the indices

      I feel like I really appreciate how this step is more involved than I originally expected - mostly in terms of needing the data to be tidied up - organized appropriately before computations.

    35. could ask how an adolescent's level of school attachment influences the likelihood of forming friendship ties

      I like this line of thinking -- how the author suggests that we don't have to feel rigid about the questions - have a little more elbow room in exploring our area of interest while still maintaining focus.

    36. deductive

      I think even with inductive approaches, the theoretical framework lends itself to identifying emerging central themes. Just gives more room to incorporate other frameworks as well...

    37. network exchange theory

      So this is a sub theory of network theory that focuses on concepts such as structural holes and brokers?

    38. If you are observing faculty members in a teachers’ lounge, are you interested in who speaks with whom, or are you also interested in who initiated the conversation? Who initiated the conversation and who responded might be more interesting than just recording the pairs of teachers who engaged in conversations.

      This is certainly applicable to my own study. My original thought was that my data could be undirected, but I'm now questioning that. For example, it would be interesting to see if an initial contact between two students was then reciprocated, or if there were other contacts that seemed to follow from the initial.

    39. Remler and Van Ryzin (2010) define theory as a logical explanation that proposes a causal process or mechanism that produces an outcome of interest

      I'm not looking for causality in my study; it's more exploratiional and inductive than that. I imagine this is driven in part by my personal usage of SNA as a tool within a study, as opposed to being the methodology of a study in and of itself.

    40. Of course, this first step will help you refine your ideas before you move forward, but you will also find useful concrete examples of how previous researchers have formulated their research questions, collected data, and analyzed those data.

      Actually, I started this project with less consulting the previous literature. At that time, I much focused on what is collectable data and how to analyze the data. When I articulate another project, I'll more deeply consult the literature

    41. One potential harm is that it is more likely than not that a participant will know who else is participating in the study

      Unless, of course, no IRB is needed and no one knows they're being studied. Which holds its own ethical issues, of course.

    42. relational measures collected at separate time points)

      Which I'm already doing on my own through weekly data collection.

    43. RSiena

      OK. So maybe this could be a useful package in the future. Except my studies will likely just stick to the semester, given I have no way of tracking students beyond that.

    44. It is strongly advised that network data be collected through means such as these because they reduce respondents’ burden and errors in data entry

      No kidding!

    45. Organization Network Analysis

      Another one to keep tabs on.

      It occurs to me that I might've been able to use one of these instead of what I did (Google Forms) to create a survey for my students and then could've already had the data ready to export rather than needing to do the entry myself. Next time...

    46. Network Genie

      Another one to keep in mind for the future.

    47. These questionnaires can incorporate name generators and interpreters and provide a very helpful framework to configure and perform a survey interview

      Wait--does this mean it takes in the data from the surveys which can then be exported into a visualization tool without the researcher needing to import data separately?? That sounds awesome!

    48. E-Net

      This one has come up in a few studies I've read as well. Probably not as useful for my particular research.

    49. RSiena

      Sounds like what I would've been using a few years ago. Maybe not as relevant anymore.

    50. PNet

      Would need to have a firmer grasp on stats to understand which elements are most applicable to me. p tests might be, but I think I recall from that week they seemed less likely something I'd be interested in, so this program may not be for me.

    51. Foremost among its strengths is that it combines attribute and relational data into one model to perform a context analysis: It integrates data that describe people with data that describe relationships between people into a single analytic model

      I don't have a large network, but am I working with more complex data. Not super complex, but I like the idea of the integration it describes.

    52. NetMiner

      I haven't seen this one come up as much, but it sounds more powerful than UCINET and still has the phrase "user friendly" in it. May be a better option to look into.

    53. easily manipulate and transform data

      I like how often the word "easy" is used here. :)

    54. UCINET

      This one comes up a lot in the studies I've read. I guess I was under the impression it was more basic (like the SNA equivalent of web 1.0) but could be wrong.

    55. (1) ties between actors in complete networks; (2) certain individual attributes predicted from relational data; or (3) relations within and between groups.

      I had some difficulty in this area with my students because they're basically meeting minimum requirements for how many reply posts they do. I could ask questions around degree of my rockstar students or maybe look at repeated ties (suggesting the formation of a relationship of sorts rather than just a "reply to random person"). I need to see how many data shook out to start forming more of those "ah-ha!"s.

    56. Visualize the Network

      Getting better at this in R. I get the basic functions (more or less). Moving onto the fancier stuff! The Michael Marin videos on YouTube were really helpful for solidifying the basics, btw.

    57. Data Matrix with Three Vectors

      Mine ended up looking more like this (but with more vectors). Much better for R. :)

    58. adjacency matrix

      This was NOT a good route for me. I made the first few Excel sheets as adjacency matrices, and R was not happy. I will NOT be using this method in the future.

    59. Once your egocentric or complete network data are gathered, they are ready to be organized in a manner that is suitable for analysis

      It took a long time to get my data all tidy, but now that I know what that looks like, it would be much easier the second time around.

    60. Valued data reflect the relative strength, frequency, or duration of a relationship between a pair of actors. Different options for gathering valued network data include the use of Likert-type scales that assess the frequency with which one engages in a behavior with someone else: 1, 2, 3, or 4; 1 = never, 4 = frequently

      I used something similar to this for coding SP and CP, which was much more meaningful for me than just binary ties. However, I'm running into issues in graphing these because SP has 3 valued elements to graph.

    61. directed or undirected relational data

      This was an easy question for me to answer. Directional all the way. I'm interested in both directions too, as a student's sense of belonging is potentially affected by both incoming and outgoing posts.

    62. With a clear question, you can develop a better image of the type of data you are interested in gathering.

      I collected all the data! Because I may be using this for multiple studies, I collected everything on a weekly basis (after the close the discussion forum for the week). It made grading much easier because I was able to code at the same time I was grading, and it helped me give more detailed feedback to my students in the grading.

      More in next section.

    63. validity, reliability, accuracy, and patterns of missingness

      Continuation from previous post...

      I can see, however, that I either need to be doing the incredibly tedious thing of coding in one session, double checking coding, and/or having a more detailed rubric to follow. Because what I was applying was not totally clearcut, I can see where I may have (and probably did) code similar things differently when it came to levels of CP and SP. Not ideal, but also helpful for ensuring better consistency in future studies

    64. Select a Sample

      I was beginning to think my answers to everything was going to be "Could've done that better...," but this was one area I did pretty well. It was more of a given, since I was teaching the class. In future studies, I could look at pairing with another instructor. That might make my coding of things like CP and SP more objective. I found myself sometimes downgrading one student whose posts were at a higher CP level because I wasn't used to them doing that level of CP in their work. I had to go back a few times to compare coding. Triangulation in the form of another coder would be helpful, but I think objectivity would help too. It's hard to do study not your own class though, given access issues.

    65. yet this order could easily also be reversed.

      Which is something Tasha pointed out to me once for my own study. It was a good point.

    66. Research questions or hypotheses flow directly from your preferred theoretical framework—assuming, of course, you are operating in a deductive manner

      As Bodong knows all too well, my issue is in narrowing. I have way too many research questions currently because I'm trying to work too many elements together. I could do a better job of isolating elements rather than trying to find some grand way they all fit together. Just because they're related doesn't mean they belong in the same study.

    67. Develop a Theoretical Frame

      When I first read this, I thought it was going to be about the 30K level stuff, which I am also still wrestling with a bit--not like stats. Different sort of brain workout.

    68. social influence theory

      This one might be particularly useful for my research. While my study is set up as being causal, it's less causal on the SNA elements themselves. I would need a more comprehensive understanding of the stats before really delving into more deductive elements.

    69. This should happen even before you start formulating your own research questions.

      I had done some review of the lit before taking this course, but I didn't understand SNA well enough to "get" the studies. Other studies I've pulled have happened after my own research began. If I were doing this process again, I would do a better job in this first step of reviewing before formulating. I would also be a little more inclusive--there are not as many studies for SNA and CoI together (so great niche to publish in, but for lit review, I should cast a wider net).

    70. art III introduced two of these theoretical areas (social capital and diffusion of innovation), but there are many others. These others include network exchange theory (Blau, 1964; Cook, Emerson, Gilmore, & Yamagishi, 1983), which focuses on how a network's structure influences whom in the network emerges as powerful, and social influence theory (e.g., Friedkin, 1998), which considers how actors influence one another's thoughts.

      Area for improvement in my project

    1. Table 7.2 will be very helpful.

    2. the intersection of the social network perspective and its emphasis on the importance of relations and the types of data that have long been preferred by mainstream social science.

      This seems to be blending qual and quant.

    1. standard, basic statistical software (e.g., SPSS, Stata, or SAS) will not give correct estimates

      So dose the basic statistical package in R work, If I am not mistaken, I think we also can't use statistical package to make estimates for relational data.

    2. This suggests that school leaders who believe that they have the capacity to have an effect are more likely to send and receive confidential exchange ties

      I hope there is a further explanation about the negative and positive result since the sender efficacy is negative.

    3. In this respect, these models are closely related to logistic regression in that they analyze a dichotomous dependent variable (1/0) that is assumed to follow a binomial distribution.

      This makes a connection to my prior knowledge about traditional statistics, binary Logistic regression(dependent variable is binary, 0 or 1) and multiple Logistic regression(dependent variable has multiple levels, just like the school leader network example, four possible outcomes).

    4. we cannot reject that null hypothesis that the number of collaboration ties sent in year 1 does not vary by the level of leaders’ trust.

      This is (unfortunately) reminding me that I need to finish my stats exam due later this week and try to come to some conclusions like this after spending probably the next 8 hours staring at awesome output in R... but I digress... yes, this seems like it would be very applicable to my study. :)

    5. Table 9.1

      wow. I'm having a Don Quixote moment of imagining that my final class presentation will have a table that looks something like this... phew.

    6. This hypothesis can be tested by comparing the average out-degree (number of dichotomized collaboration ties sent) of district- and school-level leaders.

      I have a background in quantitative statistics. The use of a t-test assumes normality of data. I know that normality does not matter for SNA, but is this particular section referring to a nonparametric test that works like a t-test or is it actually referring to the use of a t-test and just ignoring the assumption of normality?

    7. This section shifts the analytical lens to predict an individual actor's outcome, whether it is an attribute variable (e.g., a student's test score) or a structural variable (e.g., a teacher's betweenness centrality score), using relational data. For example: does a teacher's gender predict his or her influence (as measured by degree centrality)?

      Ok... So now this is making a little more sense for my own data. In my case, I could analyze the countries geographic loaction as an attribute varibale that predicts the probability of certain industries having multiple companies in a given country. Does this make sense?

    8. Similarly, the basic idea of the p* is to understand these same relations but to include actor-level and network-level attributes in the model

      I really wish there was an explanation of how to construct/create/execute a p* model using R.

    9. the focal variable is the actor's group membership expressed as an unobserved, latent variable whose value is the result of the observed ties among actors

      Ahhhh... This makes sense. I asked about this in the previous chapter.

    10. Do a leader's collaboration ties vary by their level of trust?

      Ooh! This seems like it would be very useful for my divvided up SP and CP, which I have categorized at L, M, and H as well. So, "Do students' ties vary by their level of SP?" would be the question I would ask.

    11. Therefore, this would require a MR-QAP procedure that controls for the effect of the model's second predictor.

      The difference between QAP and MR-QAP just like simple linear regression and multiple linear regression. When you have independent (network) variables more than one, MR-QAP is needed.

    12. How well does a leader's perceived level of trust in his or her colleagues predict the number of alters to whom the person sent a collaboration tie in year 1, controlling for gender and the level at which the person works (district vs. school)? This question requires three vectors of independent variables (trust score, an indicator for gender, and an indicator for level) and one dependent variable vector (collaboration year 1 out-degree).

      Oy. This (multiple linear regression) is much harder for me to wrap my brain around, but I'm going to give it a go (especially since I have so many variables and it might be useful for tying them together). Again, I have SP, CP, and discussion forum type. If I did the surveys, I'd also have perceived sense of belonging. How well does a student's perceived sense of belonging predict his/her level of CP in discussion posts, controlling for discussion forum type (for example, only looking at large group discussions)? That doesn't get the SP in there though, and may not actually be a great model of regression. :/ Help?

    13. We might even be interested in the relationship between two individual attributes among a set of actors who are connected in a network. For example, in a school classroom, is there an association between students’ engagement and their academic achievement?

      This is very similar to what my research is doing, where engagement is defined as SP and academic achievement as CP. :)

    14. P* models are often employed to take this further by including actor- and network-level covariates.

      Again, if I were to try to apply this (p) to my own data, I could look at things like gender, race, etc, but it would make more sense to see if SP was tied in--in other words, take the same scenario from p1 (if a student respond to another (say, in a large group discussion), is that student more likely to respond back when higher levels of SP are involved? I think* I applied that correctly.

    15. Using directed and dichotomized relational data, a p1 model can be used, for example, to test whether school leaders tend to reciprocate relationship choices.

      Again, attempting to apply to my own study. This one (p1)seems straightforward. If one student responds to another (in a large group discussion), is the other student more likely to reciprocate by responding to that student in future discussions?

    16. The question, therefore, is whether school leaders prefer to collaborate with those with whom they have collaborated in the past or with those that they have turned to discuss confidential issues.

      I have next to no stats knowledge, so I'm going to try to extrapolate this out in regards to my own research to try to better understand it (hopefully!). In using my own research with SP (social presence) and CP (cognitive presence). I'm going to start with the varibles: levels of SP, levels of CP, discussion forum type. A question I have been asking is whether discussion forum type affects SP and/or CP. Modeling the question the same way as this one, it might be whether students are more likely to show higher CP with students they were in a programmatic small group discussion with versus just large group. I think this models this line of questioning, at least. This probably doesn't get the SNA part in. So, trying again... Are students more likely to respond to a student in a large group discussion that they formed a connection with in a programmatic small group discussion or a random small group discussion? This doesn't get the SP or CP working in there, but it gets the SNA. So part of what I'm studying. But, since I'm graphing SP as a weighted measure for SNA, maybe it could be whether students are more likely to demonstrate higher SP in an ensuing large-group discussion with students they were in small programmatic group discussion with in a previous module. Does that get all the parts working approporiately in a MR-QAP-procedure question??

    1. A model is a simplification or approximation of reality and hence will not reflect all of reality

      when reading this, I don't know why but a question suddenly came into my mind, why do we need so complicated/fancy models in social science research, specifically, except core independent variables and outcome variables, why do we use covariate/ control variables in a given model. I had an insight from a professor's explanation: for natural science, most objects of study are homogeneous and scientists can have a good control of interference in lab environment with careful experimental design. However, in terms of social science phenomenons, they are so complicated and are impacted by so many factors, including which we already know, and also a lot of which we don't know yet, let alone the subjects of social science study are so unique and heterogeneous. So we have to use advanced model to get closer to understanding those complex phenomenons, and we have to try our best to control the covariates we already know to carefully test the real relationship between independent variables and dependent variable. In addition, because we can not know or measure all factors that will impact a certain complex phenomenon, this is one of the reasons that a model is a simplification or approximation of reality and hence will not reflect all of reality.

  4. Mar 2017
    1. Where statistics really become “statistical” is on the inferential side, that is, when attention turns to assessing the reproducibility or likelihood of an observed pattern

      This sentences articulates the key character of statistics, I like it.

    2. there is little apparent difference between conventional statistical approaches and network approaches

      This sentence answers a question I proposed a few weeks ago.

    3. These measures convey the image of a fixed network with attitudes or behaviors passing through it,

      Since my own study takes these measures weekly, I'm curious to see how the statistical aspect may help draw connections between weeks and predict what could happen in future weeks.

      Although, I will say I think this is only true in a broad sense. For example, I think I may be able to predict from a couple weeks of SNA data who the central players in future weeks will be (rock star students tend to retain that status throughout), but it may not be able to predict who responds to whom. Though such a measure may not be all that useful anyway.

      Coming back to this after reading the chapter, I'm having a difficult time deciding whether statistical approaches make sense for me. I am trying to make generalizations, but it's not around categories (like gender, race, etc.). Unless maybe my category is SP (social presence)? To make a prediction about students with high SP having high CP (cognitive presence)?

    4. Contrast the aims of the mathematical and statistical approaches to social network analysis. For what reasons would educational researchers prefer one approach versus the other?

      Mathematical is descriptive (centrality, degree, etc), whereas statistical is inferative (allowing to make a prediction about the network). A researcher would be interested in the former if they do not need to make generlatizations about their network--if they are just interested in their network for its own sake. If the researcher wanted to say something about the population in general based on their network, then they would want to consider integrating statistical approaches.

    5. Explain in plain language how simulations are used to create a probability distribution that enables you to make a statistical inference with network data.

      If your network differs from a typical random creation, then you can perhaps make a claim about unique properties.

    6. Why are these simulations necessary in order to make probabilistic inferences with network data?

      Simulations make things a lot easier. In even a modestly sized network, there could be thousands of permutations. Having a tool to create randomizations makes the process much less cumbersome.

    7. According to this emphasis, the main question asked is: If a study is repeated on a different sample (drawn by the same method), how likely is it that you would get the same answer about what is going on in the whole population from which both samples have been drawn?

      For my own study, this would be making a claim about the connection of SP to CP in certain discussion forum types to suggest that you can get the biggest bang for your SP and CP buck by adopting a certain discussion forum type.

    8. So, let's say you are interested in the number of collaborative exchanges that occur between teachers from two different grade levels in a complete network of teachers within one elementary school. First, you count the number of times these types of exchanges occur in the observed network and then permute these relational data lots and lots of times. With each permutation, you calculate the number of times this type of tie (collaborative exchanges between teachers from two different grade levels) occurs and compare this result to the original observed network. After this process of permuting and comparing, you can see how often the results of these permutations are the same as the original observed results: The more often the results of the permutations are the same as your observed data, the more likely that the pattern of exchanges in the observed data was due to chance. If, however, the results from the observed data are so unlikely when compared to the results of the permutations, then you are to conclude that your results are not the byproduct of chance. Therefore, this result would be considered statistically significant.

      In terms of my project (looking at racial and gender-based biases in communication between undergarduate students in an online class), then I could use this same rationale and process in order to make generalizations to a broader population?

    9. In many cases, network analysts are studying a particular network or set of networks and have no interest in generalizing to a larger population of such networks (either because there isn't any such population or because they simply do not care about generalizing to it in any probabilistic way)

      I'm in this category with my own research outside fo this class.

    10. equilibrium” status of the network

      But didn't this just get done talking about how networks in the real world tend to change and shift? What would the value of this approach be, then, especially in regards to education research where, by its very nature, networks change often?

    11. The distinction between the two, however, is not clear cut.


    12. Part II

      this means Part II of this book instead of this chapter. Just fyi #SNAEd

    13. the difference between the mathematical and statistical approaches to social network analysis

      What are the differences?

    1. definition(s) of the neighborhood

      For my project, I am looking only at groups of learners in a class room, so because the group is already so small, analysis of 1 and 2 step neighborhoods should be fine.

    2. How ego-centric networks could be applied to your research projects?

      In my case, ego-centric networks are particularly interesting. I am looking forward to see how different individuals from first language groups interact with learners from other first language groups. Also, how does this correlate to their English proficiency, grades etc.

    3. definition(s) of the neighborhood

      Since my groups are small with four to six participants, a one-step neighborhood probably makes most sense, but if I were to look at an egocentric analysis with the moderator as the ego, the larger or complete network would be more applicable?

    4. What definition(s) of the neighborhood will make sense for your research projects?

      A few could apply. Honestly I might need to do a lot of playing with my data to see how/if ego-centric networks will actually give me any meaningful measures. I'm interested in some of the brokerage concepts which is making me rethink a lot of what I've done so far.

    5. How ego-centric networks could be applied to your research projects?

      Egocentric networks could be applied to my assignment in various ways. One of the more interesting ones would be to compare similarly titled jobs (i.e. Instructional Designer) within different organizations (OIT v. College level) and/or to compare similarly connected individuals and determine their titles and positions to see if there are any takeaway commonalities.

    6. What definition(s) of the neighborhood will make sense for your research projects?

      I would prefer to define the "neighborhood" for my ego-centric analysis to one-step, because I am most interested in who that person is directly connected to, instead of looking at the larger network that the ego is nearly connected to.

    7. How ego-centric networks could be applied to your research projects?

      If I were to apply ego-centric networks to my project, I would focus in on studying members of the game development community that have gone through a public education program to see if they have become more well-connected in the community since going through the program.

    8. applied to your

      I could use ego-centric network analysis in my research by taking each individual ego and exploring the friendships between the each individual ego in relation to the network. Working with 2 smaller groups of 4 participants, I have already considered doing this, but this chapter has given me more ideas on how to incorporate this type of SNA. I would also like to explore the connection between egos coupled with any ties between alters.

    9. How ego-centric networks could be applied to your research projects?

      Since I am looking at teacher interactions across four groups in two years, I could use ego-centric networks to explore the role of particular actors -- to answer questions related to how particular actors influence interactions based on specific aspects of certain attributes - by looking at the quality/quantity of their initiated interactions for example. Another example is to look at the moderator's role -- each year there was a different but consistent moderator leading all four groups. Categorizing the moderator's interactions based on either quality or quantity could also be very revealing?

    1. Alternately, you would also use an egocentric approach if your research question is about different patterns of interaction within defined groups

      Comparing different groups with the same or similar structure? Or differing patterns of interaction within the groups? Or both?

    2. Egocentric analysis shifts the analytical lens onto a sole ego actor and concentrates on the local pattern of relations in which that ego is embedded as well as the types of resources to which those relations provide access.

      Given the nature of my data (Forbes top companies), I think it would be appropriate to look at specific countries as the Ego and the job categories as the alters. Am I correct in assuming that the local pattern of relations would be how my selected county (the ego) is connected to other countries through job category?

    3. it is possible to examine directed relations in egocentric network studies, or what are referred to as out- and in-neighborhoods: ties sent or ties received

      Since I am working with countries and job categories, I think it would be best for me to work with non directional neighborhoods. I would like to see some directed ego centric data though.

    4. gocentric analysis is primarily concerned with describing how individuals are embedded in local social structures and, ultimately, how these individual indices of social structure relate to varied outcomes

      My research doesn't actually focus on individuals so this is interesting.

    5. Summary

      When looking at my data set, I'm most curious to analyze the ego networks of particular students that either do not feel connected to the school or regularly struggle to academically achieve. Perhaps individual interventions can be designed to aid the educational experience of these students.

    6. advantage

      The term 'advantage' seems to be used loosely. I understand the power that A may be able to exert over students B & C, but given the various values of weak/strong ties and dense/disperse networks, I do not totally see how the term 'advantage' always applies in this scenario.

    7. Stated another way, weak ties are important for transmitting information but less so for transmitting behavioral influence (Valente, 2010)

      This is the most succinct description of the value of strong and weak ties I have heard yet.

    8. This translates into the number of “steps” that separate the two most distant actors in an ego's network.

      My intuitive analysis suggests that there must be some easy way to convert between density and distance. I am probably missing a core difference between density and distance for ego networks. The difference here doesn't seem to be as valuable as the difference for full networks.

    9. Viewed from this perspective, dense networks reinforce prevailing norms and behaviors and insulate one from outside influences (these can be, however, either good or bad)

      A similar theoretical foundation may apply to 'echo chambers' noted recently throughout social media.

    10. The size of ego networks typically ranges from 0 to 6, since a name generator typically limits the number of alters that ego can list.

      Is it not valualbe to analyze the size of ego networks for social media? This likely wouldn't limit the size of the networks to 6, right?

    11. Table 7.2

      There seem to be two types of questions here: relational questions and attribute questions. Might it be worth categorizing these questions about eliciting egocentric network data into two seperate categoies?

    12. Table 7.1

      I notice that Leader 35 has the smallest number of alters and the highest efficacy score. Did I read that correctly? I am sure this is too tiny of a data set to conclude anything, so I'm curious what would happen to that pattern with broader analysis.

    13. Brokerage

      This whole concept is a huge part of what I want to get out of my data set! This is exciting and also terrifying because I will have to figure out how to actually get these measurements....

    14. so that the ego networks of tenured teachers could be compared to the ego networks of untenured teachers

      I am going to see if it makes sense to do something like this with my data in terms of OIT v. non-OIT or instructional designers v. other types of employees. So many potential ideas!!!

    15. Ego actors can be individual persons, groups, or even some larger entity

      I had not thought of that until they spelled this out. that actually makes a lot of sense.

    16. here are several standard measures that can be calculated from egocentric network data, including size, strength, diversity, centrality, constraint, and brokerage.

      I believe SIZE and DENSITY are very intuitive measures that enable readers to understand the characteristics of eco-centric networks, especially, when we try to compare the different eco-centric networks. My related question is "how we can statistically compare those values from different networks?". To expand the qustion, "how we can statistically test the differences among different networks?"

    17. The first is an “out neighborhood,” which includes all the actors to whom ego sends a tie. Conversely, a directed ego network can be defined as an “in neighborhood,” which simply includes all those actors who send ties to ego.

      Thinking about direction makes sense for my study. It could be interesting to compare an ego-centric network to look at one student (Student A) to see if Student A's out and in neighborhoods are the same or mostly overlap. From what I've seen in my data so far, this has only been the case when students are placed into small groups. When it's the discussion forum as a full class, there is significantly less overlap.

      It could be interesting to compare students' sense of belonging/community on the basis of whether they have a strong in or out neighborhood. I could see students who have high out but low in as feeling disconnected, and students with high in but low out maybe not even paying attention/not really caring about sense of belonging. Methinks I need to add a survey to my research...

    18. Why Study Ego Networks?

      This may or may not make sense for my research. Since my network is small (19 students; 20 if you count me, the instructor), it's easy enough to look at the whole network (socio-centric) and then decide if it makes sense to zero in on particular students. Reasons I may want to use ego-centric: To study high-performing (vs low-performing) students' networks; to isolate students showing high cognitive presence (one of the aspects I'm researching) to see what their individual networks look like; to explore students with high social presence to see what their networks look like--does quality of SP go along with quantity of SP?

      If I were administering surveys (which I do not believe I'll have time to do), then I might want to look at egos based on survey results, for example, to a ask like "Do you feel a sense of belonging in this online class?" to study how students' perceptions of belonging align with their actual quantity and quality of ties.

    19. Table 7.1

      The data presented in these tables is often kind of confusing, if it is generated from the program they are using.

    20. In addition, most analyses of ego networks use binary data: Two actors are either connected or they are not. This makes the analytical task of defining an ego's “neighborhood” much easier. However, if the relational data between ego and alter is valued—that is, the strength of the tie has been measured—you have to decide the point at which a tie exists.

      This doesn't seem specific to ego-level analyses, since this type of work needs to be done with network-level analyses as well.

    21. Whereas the previous chapter focused on concepts and measures most appropriate for complete network analysis, this chapter shifts the perspective to the analytical level of a sole focal actor—ego.

      Is it common to perform both types of analyses on a dataset?

    22. the density

      I can't insert permutation and combination formula here, I will try to explain my confusion. I don't understand why use C 5 3 instead of C 5 2, although C 5 3 equals to C 5 2, I still think only using C 5 2 can make sense in this setting. Maybe I get something wrong, please correct me if so.

    23. This was calculated by summing the geodesic distances between School Leader 1 and the other 42 actors in the network, dividing this by 1, and then multiplying it by 42, (g – 1).

      I think we calculated closeness centrality of vertices (and also the closeness centralization of complete network) two week s ago, it used the similar computation method as this egocentric network closeness centrality. It seems like that when calculate closeness and betweenness, you have to include indirect connections of a given ego, not just direct connections in the egocentric network.

    24. as they each capture a different view or purpose of centrality.

      Based on what I have learnt, betweenness captures brokerage, and closeness captures reachability (it is about how far away the rest of the network is from a certain actor). However, we also talked about eigenvector centrality a few weeks ago (the thought of eigenvector centrality is that your importance is determined by your neighborhoods’ importance) , I was wondering why the author didn't introduce eigenvector centrality of egocentric network in this chapter. I think one actor's eigenvector can indicate this actor's potential value or importance, it can be very useful in some specific settings.

    25. Tie strength has been a core idea throughout the network field, with weak ties serving as important bridges between different groups and strong ties being influential in behavioral adoption. Generally, weak ties are important for the spread of instrumental resources (e.g., work-related advice), while strong ties are important for expressive resources (e.g., guidance on personal matters) (Lin, 2001a). Stated another way, weak ties are important for transmitting information but less so for transmitting behavioral influence (Valente, 2010). Granovetter's (1973) classic work has laid much of the foundation for much of the work that has focused on the tie strength.

      I can see some application of tie strength both in my project for this course (analyzing the effects of gender and race on interaction between actors) and beyond my dissertation research: how do technology integration processes disseminate through a network of teachers?

    26. Finally, while most analyses of ego networks use simple graphs—binary data that simply indicate whether an undirected tie is present between two actors—it is possible to incorporate directed relations into ego network analysis.

      Could one use bidrectionality of connections as a emasure of density?

    27. Egocentric network data generated in this manner, however, cannot be used to describe the overall embeddedness of the networks in some larger population.

      Seems similar to a caution when using Case Study qualitative research methods - it's best to use caution when generalizing from case study research.

    28. Egos and Alters

      At the risk of being off topic - why is it that academics insist on finding new terminology within their specific domain? Seems to me that, in general, this is a barrier to the disemination of knowledge and information across disciplines....

    29. These less dense networks, often referred to as radial networks, can also be favorable or unfavorable, depending on the behavior or attitude that you are interesting in studying

      This reminds me of strong ties theory and weak ties theory(also called structure hole) we had read before which are competitive theories , but both theories can explain some certain social phenomenon. Strong ties theory can explain how strong ties affect people's behavior or attitudes etc., and weak ties play a role of bridge to disseminate (non-redundant) information. So I think what matters is your research question/interest, your research question will drive you to apply appropriate theory and interpretation.

    30. two types of measures

      These two types of measures sound like the traditional statistical methods analyzing independent variables(to get some descriptive results of central tendency and tendency of dispersion). However, I think egocentric data is relational data which violates independence assumption. What are the differences between relational data analysis and independent data analysis. I am not sure whether I propose my question appropriately and correctly, and I remember I had read some detailed information relating to this question before, but I am still confused about it. (According to these examples listing in this paragraph, maybe the author talks about attribute data, not relational data?)

    31. This is the simplest type of ego network data, which makes defining the ego neighborhood a straightforward task.

      In my project, I will define the ego neighborhood in this straightforward way--ego and alter are linked if they were co-authors on a published paper. I would like to explore the ego-centric network of a few top productive authors, to analyse their collaboration patterns. Specifically, do these top productive authors tend to have cross-disciplinary partnerships, inter-institutional partnerships, or with-in institutional partnerships?

    32. “agent” in relations across groups

      I think this also applies in financial sectors, right? good explanantion linking centrality and structural holes; I also like the breakdown in the bottom paragraph with five specific roles shown in fig. 7.7

    33. constraint, extends the egocentric network density measure to include more information about the structural pattern of relations among ego's alters.

      density + pattern of relations among alters = constraints

    34. eigenvector centrality (Bonacich, 1972), entropy (Tutzauer, 2007), power (Bonacich, 1987), Katz centrality (1953), and random-walk centrality

      wow. entropy sounds awesome. I want to be able to use this in my analysis just because it sounds so cool and I think it was one of the few concepts that really made sense when I was introduced to entropy in physics and chemistry!

    35. able 7.2 Types of Questions Used to Elicit Egocentric Network Data.

      This is useful. Sometimes I wish different studies would just show us the surveys they used so I can get a clear idea of types of questions and what is visualized.

    36. (1) the topography of an ego's network and (2) the composition of that network, including the attributes of the alters to whom ego is connected.

      focus of questions that try to examine individual entities across different networks and/or patterns of interaction within groups.

    37. an individual's (ego) connections with others (alters) provides access to some instrumental (e.g., advice) or expressive (e.g., support) resource that may, in turn, be beneficia

      ego's social capital in the hood

    1. There is no assumption that groups are the building blocks of society: the approach is open to studyingless-bounded social systems, from nonlocal communities to links among websites.Rather than treating individuals (persons, organizations, states) as discrete units of analysis, it focuses on howthe structure of ties affects individuals and their relationships.In contrast to analyses that assume that socialization into norms determines behavior, network analysis looksto see the extent to which the structure and composition of ties affect norms.

      This is important for my own understanding of the "Why" of SNA. It made sense to disengage the social portion of the practice and focus on the composition of the ties. An ah-ha.

    1. How centralized is the network?To what extent is there a small number of highly central nodes?

      It is easy to see why node level centrality and network-level centralization are mistakenly treated as the same. This is my third or fourth time through this and I'm still not completely clear.

    1. Figure 6.5

      Honestly, this is what I want to know when I analyze sub-groups in a network. It is a good logical process that I can follow and cite as the process in my research. Appreciate it!

    2. By charting this process, you are able to identify whether there is a “core” group of actors at the center of the network, while others are on the periphery

      To identify "core" actor, what is a difference between a K-core collapse and centrality. I understand that there are difference logics between them. However, considering the expected results, I am not sure how we differenciate those different techniques.

    3. A weak component ignores the direction of a tie; strong components do not. Stated differently, strong components consist of nodes that are connected to one another via both directions along the path that connects them. Weak components consist of a set of nodes that are connected regardless of the direction of the ties.

      Which means, what I understand that, there is no diffence between a strong and a weak component if a network is undirected. Is it right?

    4. It should be informed by theory

      I'm not sure what theory I should be leveraging to make cut-offs in my analysis of Slack interactions.

    5. top-down” and “bottom-up

      These are two terms that are commonly used in literacy in regards to acquisition of emerging readers and two different theories as to which is the best way to help young readers become successful in reading. Spoiler - bottom-up won...

    6. cut-off value

      Although I already determined I needed to add a cut-off value to my data in order to make some of it meaningful it's reassuring to read about here.

    7. something that Position 4 seems to possess. Position 3 (Students 5, 3, and 9) has a reciprocated relationship with this position, suggesting that this actor, too, may reap some advantages by having a mutual tie with these actors; no other position receives a tie from them

      When designating each of the eight positions, was there a structure to the numbering? In other words, why does Position 4 seem to possess more influence? Why wasn't this group named "Position 1"? It seems random. I am just curious if I'm missing something.

    8. In Figure 6.5, there are actually five automorphic equivalence positions: {A}, {B, D}, {C}, {E, F, H, I}, and {G}

      Though noteably different, it seems like {B, C, D} might also hold reasonably equivalant positions. What analysis would show such positionality?

    9. Though each procedure will provide you with a different take on the network's substructures, they are all based on the ways in which actors are interconnecte

      What would result from analyzing the similarities and differences between the results from each of these methods of group analysis? Each is distinct, for good reason. Nevertheless, overlap may rightly be expected. Might there be a way to identify that overlap and then make mearning from the overlap?

    10. By relaxing the criteria for group membership, the number of cliques has increased from 11 to 20, with Student 1, for example, being a member of 16 clique

      This seems to be an unweildy number of cliques for a group of 17 people. How can this bottom-up analysis conclude similar findings as a simple ethnographic inquiry? Would more analysis be necessary? If so, what analysis?

    11. In general, GN subgroups are identified by first calculating betweenness centrality (this version of centrality is discussed in Chapter 7) on the ties, and, second, determining if there are any components revealed once ties with the highest betweenness scores are removed. This process is repeated until the number of desired groups is obtained

      This seems to answer my question about the authors decision to have three blocks in this data set. Nevertheless, I need to practice this calculation to shake the feeling that it is ultimately arbitrary.

    12. The number of “blocks” to enter is up to you, and after working upward from two, it was decided that three was a meaningful number of blocks.

      I trust that there was a theoretical framework for this choice. Nevertheless, I am at a loss to understand that framework. Why were three blocks chosen?

    13. However, you might also want to reveal how groups are distributed in the network and which actors belong to which groups. A clique analysis is one way to satisfy these purposes. A clique is a maximally connected subgraph of nodes (> 2) in which all nodes are connected to each other.

      It may be interesting to apply this after I've had students in the same small groups for a couple weeks for their discussions and then return to a whole-class discussion (to see if the students respond more so to the students they had been in a group with previously).

    14. Therefore, a 3K-core is a substructure, a subset of actors, in which each node is connected to at least three other nodes; a 2K-core would be a subset in which a node is connected to two others, and so forth. Those nodes that do not meet K, which is defined by you, are dropped from the network

      I could see this being useful for looking at my own research. Not many of the students are interacting beyond the minimum requirements for responses (typically, 2), so I could use this to separate out those who are a bit more easily (so doing a 3K-core).

    15. Equivalence, in general, refers to actors who occupy the same position.

      Nice definition

    16. for two high school teachers to occupy a structurally equivalent position, both teachers must teach the same set of students

      This reminds me of my middle school. Rather than each class period having different students, we had one classroom with the same students and we would just switch teachers each period. They all taught the same students. It's nice to have a personal example of structural equivalence

    17. equivalence

      I'm interested to learn more about this.

    18. top-down

      In ELT, "Top Down" refers to getting learners to make predictions about reading/listening activities before they actually do them. For example showing students a magazine article and asking to predict what they the article might be about, based on pictures or titles. This seems to be similar here, looking at readily available information without getting into the details first.

    19. A Visual Comparison of Structural, Automorphic, and Regular Equivalence. Consider this a graph of a hypothetical hierarchy of a school district's organizational chart, which consists of three levels linked by supervisory relation. Depending on your preferred definition of equivalence, different positions will be identified.

      Moving forward into post-dissertation work, I can see how the idea of equivalence might apply in looking at technology integration processes across schools. But, for my research questions posed in this class, this isn't very useful.

    20. ou can also increase the value of n, but this is not advisable, as it seems odd for actors to be in the same clique if they are three steps from one another.

      While I certainly understand this in a real-world sense, I do interact with friends-of-friends in social media, either directly or through group membership. As such, is it acceptable to increase n when doing research, if one's research questions/interest require?

    21. By charting this process, you are able to identify whether there is a “core” group of actors at the center of the network, while others are on the periphery.

      Does anyone know of a way to illustrate this in a GIF or similar? I guess maybe doing it manually could be workable, but it sure seems to me that automating the charting of this process would make for increased ease when discussing research online.