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.

can confirm. was in high school once.

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

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.

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?!

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.

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.

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.

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....

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!!!

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

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.

This one was easy! Getting good a good directed network to play around with it with into R and able to be modified was... way harder than getting this info.

reciprocity(g, ignore.loops = TRUE)

There is an additional mode operator where if you put the mode = ("ratio") it calculates (unordered) vertex pairs are classified into three groups: (1) not-connected, (2) non-reciprocaly connected, (3) reciprocally connected. The result is the size of group (3), divided by the sum of group sizes (2)+(3).

This is the hard part! Who/what participants at the University represent range from "college" to "department" to "faculty groups" to "student union" to "tight knit group of people who happen to teach online courses". how to collapse them is hard to quantify, even if you know who they are and how they're related to various things.

this is my problem. most of my ability to collect data relies on this (i.e. I know who attended an event) but the event attendees are the people I don't need to reach or concentrate on. They already attended! How to study the real social networks of people who are interested or invested in an event but can't/won't/don't attend is what I need.