TWeD Talk: Who are the Influencers? New Algorithms for Detecting Key Players in Social Networks

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TWeD Talk: Who are the Influencers? New Algorithms for Detecting Key Players in Social NetworksJanuary 27, 2014
There's always something happening on Wednesday evenings in the Tetherless World!

TWeD Talk, Wednesday, January 29, 2014, 7pm ET, Winslow Building on the RPI Campus

Please join us this week as new TWC postdoc Xiaohui Lu leads us in what promises to be an interesting discussion of his work developing novel social network analysis algorithms.

ABSTRACT: One of the primary tasks of social network analysis is the identification of the influential actors in a social network. Centrality measures based on one's structural position, such as betweenness, closeness and degree centrality, are widely applied to various social networks for this purpose. However, these measures often suffer from prohibitive computational cost, non-intuitive assumptions, and limited applications. Meanwhile, with the explosive emergence and the widespread accessibility of online social network sites, large scale networks with multiple types of entities, such as author-publication, actor-movie, employee-email networks, are ubiquitous and readily available. However, due to size and multiple modes, classical centrality measures are helpless in such cases.

In this talk, I first present algorithms for pure social networks (actor-actor networks), then an algorithm for multi-mode networks. In pure social networks, centrality algorithms are good candidates. However, these centrality measures suffer from several issues - they either look solely at the structure of the network disregarding issues like attention nodes have to give to others or make a shortest path interaction assumption that might be impractical in large networks. Algorithms for pure social networks are not able to take advantage of abundant information hidden in multi-mode (heterogeneous) networks. I developed an algorithm to analyze such heterogeneous networks. The algorithm iterates from one type of objects to another, and importance of objects flow through these different types of edges. This algorithm is based on empirical observations - influential actors are likely to collaborate with influential others; good collaboration product tends to be in good groups.

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