27 Matching Annotations
  1. Apr 2025
    1. An alternative proof

      My Proof: Based on conservation law, split the network into s, L, R, t with nflow(L) = nflow(R) = 0, nflow(s) = excessive(t) for any split, thus max-flow <= min-cut.

      Given a max-flow, remove an edge and reducing flow value from all down-stream flowing edge repetitively. Eventually total flow value of removed edge must = nflow(s) and it must be a cut (otherwise the original flow can be arugmented and not maximized).

    2. Lemma 8.3. For any A ⊆ V (G) such that s ∈ A, t /∈ A, and any s-t-flow f ,(a) value ( f ) = ∑e∈δ+( A) f (e) − ∑e∈δ−( A) f (e).(b) value ( f ) ≤ ∑e∈δ+( A) u(e)

      Better Proof: The (net) outflow of a set of vertice equal the sum of net flow of each vertice (Just write the forumla) and is thus equal to zero due to conservation law. So nflow(A, s) + nflow(A, V \ A \ s) = 0 the former is = - nflow(s).

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  2. Nov 2024
    1. How to Spot Emerging Note Clusters Without Alphanumeric Note Numbering? by [[Ton Zijlstra]] in Interdependent Thoughts

      I recall Bob Doto had a video at some point in which he used the local graph to show relationships to find bunches of notes for potentially writing pieces or articles as indicated in Tons' article.

      One of the biggest issues with digital note taking tools is that they don't make it easy to see and identify chains of notes which might make for articles, chapters, or books.

      Surely there must be some way to calculate neighborhoods of notes from a topological perspective? Perhaps if one imposed a measure on the space to create relative distances of notes?

  3. Feb 2023
    1. fz is less about the tree (though that is important) and more about the UX.

      I do like the framing of folgezettel as a benefit with respect to user experience.


      There is a lot of mention of the idea of trees within the note taking and zettelkasten space, but we really ought to be looking more closely at other living systems models like rhizomes and things which have a network-like structure.

  4. Jan 2022
    1. https://www.youtube.com/watch?v=z3Tvjf0buc8

      graph thinking

      • intuitive
      • speed, agility
      • adaptability

      ; graph thinking : focuses on relationships to turn data into information and uses patterns to find meaning

      property graph data model

      • relationships (connectors with verbs which can have properties)
      • nodes (have names and can have properties)

      Examples:

      • Purchase recommendations for products in real time
      • Fraud detection

      Use for dependency analysis

  5. Mar 2021
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  13. Apr 2017
  14. Sep 2016