Reading Club session 6 August 2020

We discussed two classic papers by Alex Graves. The oldest of these was “Generating Sequences With Recurrent Neural Networks” in which we especially focused on those parts about generating handwriting. The newer one, “DRAW: A Recurrent Neural Network For Image Generation” had several coauthors from Google DeepMind and explained how to use neural Turing machines …
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Reading Club session for 23 July 2020

We wrapped up our discussion of clustering on graphs, still with a couple more sections from “Clustering and Community Detection in Directed Networks: A Survey” by Fragkiskos D. Malliarosa, Michalis Vazirgiannis. For the second half of the session, we switched gears and discussed a comparison between polynomial regression and neural networks that seemed both theoretically …
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Reading Club session for 2 July 2020

We continued with our study of clustering methods applied to graph theory with “Clustering and Community Detection in Directed Networks: A Survey” by Fragkiskos D. Malliarosa, Michalis Vazirgiannis which describes methods for clustering graphs without discarding directional information.

Reading Club session for 28 May 2020

Here we began a study of clustering methods applied to graph theory with the goal of understanding community detection in social networks. We began with an application from Oggier, Phetsouvanh and Datta, “BiVA: Bitcoin Network Visualization & Analysis” which applies clustering techniques and entropy centrality to produce visualizations of the transaction network defined by the …
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Reading Club session for 14 May 2020

We continued studying graphs with social networking applications in mind. We read a paper with obvious importance to social networks, “Opinion maximization in social networks” by Aristides Gionis, Evimaria Terzi and Panayiotis Tsaparas. This paper described a game theoretical method for choosing members of a social network who can spread a change of opinion most …
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Reading Club session for 21 April 2020

We continued studying graphs with social networking applications in mind. We read “On efficient use of entropy centrality for social network analysis and community detection” by Alexander G. Nikolaev, Raihan Razib, Ashwin Kucheriya which described a measurement of centrality. Think of a social network as network of merchants trading apples with their neighbors. Consider what …
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PageRank

Several members of our reading club had expressed interest in the problem of community finding in social networks. The obvious direction to go was to look at graph clustering algorithms, but first we needed to cover some of the basics of graph analysis. Hence, we began with the classic paper on the PageRank algorithm, “The …
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