  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.
