On The Network You Keep: Analyzing Persons of Interest using Cliqster

TL;DR

This paper introduces Cliqster, a novel generative model for analyzing and classifying networks of persons of interest, demonstrating its effectiveness in distinguishing different categories of such networks.

Contribution

The paper presents Cliqster, a new Bernoulli process-based model that captures community structure and improves network classification over existing methods.

Findings

Cliqster outperforms SVD and Graphlet algorithms in network classification.

The model provides an interpretable and concise network representation.

It effectively distinguishes between various categories of 'anti-social' networks.

Abstract

Our goal is to determine the structural differences between different categories of networks and to use these differences to predict the network category. Existing work on this topic has looked at social networks such as Facebook, Twitter, co-author networks etc. We, instead, focus on a novel data set that we have assembled from a variety of sources, including law-enforcement agencies, financial institutions, commercial database providers and other similar organizations. The data set comprises networks of "persons of interest" with each network belonging to different categories such as suspected terrorists, convicted individuals etc. We demonstrate that such "anti-social" networks are qualitatively different from the usual social networks and that new techniques are required to identify and learn features of such networks for the purposes of prediction and classification. We propose Cliqster, a new generative Bernoulli process-based model for unweighted networks. The generating probabilities are the result of a decomposition which reflects a network's community structure. Using a maximum likelihood solution for the network inference leads to a least-squares problem. By solving this problem, we are able to present an efficient algorithm for transforming the network to a new space which is both concise and discriminative. This new space preserves the identity of the network as much as possible. Our algorithm is interpretable and intuitive. Finally, by comparing our research against the baseline method (SVD) and against a state-of-the-art Graphlet algorithm, we show the strength of our algorithm in discriminating between different categories of networks.