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Stochastic Blockmodel with Cluster Overlap, Relevance Selection, and Similarity-Based Smoothing
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BibTeX
@MISC{Whang_stochasticblockmodel,
author = {Joyce Jiyoung Whang and Piyush Rai and Inderjit S. Dhillon},
title = {Stochastic Blockmodel with Cluster Overlap, Relevance Selection, and Similarity-Based Smoothing},
year = {}
}
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Abstract
Abstract—Stochastic blockmodels provide a rich, probabilis-tic framework for modeling relational data by expressing the objects being modeled in terms of a latent vector represen-tation. This representation can be a latent indicator vector denoting the cluster membership (hard clustering), a vector of cluster membership probabilities (soft clustering), or more generally a real-valued vector (latent space representation). Recently, a new class of overlapping stochastic blockmodels has been proposed where the idea is to allow the objects to have hard memberships in multiple clusters (in form of a latent binary vector). This aspect captures the properties of many real-world networks in domains such as biology and social networks where objects can simultaneously have memberships in multiple clusters owing to the multiple roles they may have. In this paper, we improve upon this model in three key ways:
Keyphrases
relevance selection stochastic blockmodel cluster overlap similarity-based smoothing multiple cluster many real-world network real-valued vector new class latent space representation social network probabilis-tic framework abstract stochastic blockmodels stochastic blockmodels key way hard clustering hard membership soft clustering latent binary vector cluster membership multiple role latent vector represen-tation relational data latent indicator vector cluster membership probability
