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.
 The aim of this paper is to investigate whether weak ties play an important role in explaining criminal activities. We first develop a model where individuals learn about crime opportunities by interacting with other peers. These interactions can take the form of either strong or weak ties. We find that increasing the percentage of weak ties induces more transitions from non-crime to crime and thus the crime rate in the economy increases. This is because, when the percentage of weak ties is high, delinquents and non-delinquents are in close contact with each other. We then test these predictions using the U.S. National Longitudinal Survey of Adolescent Health (AddHealth), which contains unique detailed informations on friendship relationships among teenagers. The theoretical predictions of our model are confirmed by the empirical analysis since we find that weak ties, as measured by friends of friends, have a positive impact on criminal activities. The high degree of variance of crime rates across space (and across time) is one of the oldest puzzles in the social sciences (see Quetelet (1835)). Our empirical work strongly suggests that this variance is not the result of observed or unobserved geographic attributes. This paper presents a model where social interactions create enough covariance across individuals to explain the high cross- city variance of crime rates. This model provides a natural index of social interactions which can compare the degree of social interaction across crimes, across geographic 1units and across time. Our index gives similar results for different data samples and suggests that the amount of social interactions are highest in petty crimes (such as larceny and auto theft), moderate in more serious crimes (assault, burglary and robbery) and almost negligible in murder and rape. The index of social interactions is also applied to non-criminal choices and we find that there is substantial interaction in schooling choice.