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Data88_LMATA
  • Course Description
  • Poster Gallery
  • Network Graphs
Data88_LMATA
  • Course Description
  • Poster Gallery
  • Network Graphs
  • More
    • Course Description
    • Poster Gallery
    • Network Graphs

Network Graphs

  • Gephi & Gephi on GitHub (https://gephi.org/) (https://github.com/gephi/gephi)

  • Hanneman. Introduction to social network methods, Chapter 10. Centrality and power: (https://faculty.ucr.edu/~hanneman/nettext/C10_Centrality.html#Bonacich)

  • NLTK, Chapter 4. NetworkX: (https://www.nltk.org/book/ch04.html#code-networkx)

  • MML, Chapter 4 & 8. Matrix Decompositions & When Models Meet Data (https://mml-book.github.io/book/mml-book.pdf)

  • Weingart. 2011. "Demystifying Networks" (http://www.scottbot.net/HIAL/index.html@p=6279.html)

  • Brughmans. 2013.Thinking Through Networks (https://drive.google.com/open?id=14pP3QHHVAeFj7W304fUs4q6c5N3Q3B93)

  • Gephi Tutorial Quick Start. 2010. (https://gephi.org/tutorials/gephi-tutorial-quick_start.pdf)

  • Cherven. 2015. Mastering Gephi Network Visualization (https://drive.google.com/open?id=1dpCGCCDwRCHBJytUIlwcflZ3lUbRM-9y)

  • More Gephi Tutorials: (http://www.martingrandjean.ch/introduction-to-network-visualization-gephi/)

Weekly Outline Spreadsheet

Data88_LMATA

Language Models Visualized

Adam Anderson: GitHub/admndrsn/AWCA

To illustrate how a visualization can be expressed in a poster, I'll take an example of the series of Language Models my DS-Discovery team (AWCA) recently made. The corpus or archive consisted of 1346 documents in the field of Assyriology. The links (edge list) for the networks were formed from scores from three different language models: 1) TopicModel (LDA), 2) Doc2Vec, 3) BERT. The network visualizations allows for greater interpretation and manipulation of the related document clusters, labeled proportionately. It also provides an ensemble of statistical analysis, e.g., Betweenness centrality, and clustering coefficients seen for each model below.

Student Poster Projects

(2020)


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