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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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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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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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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