We present a family of algorithms to uncover tribes—groups of individuals who share unusual sequences of affiliations. While much work inferring community structure describes large-scale trends, we instead search for small groups of tightly linked individuals who behave anomalously with respect to those trends. We apply the algorithms to a large temporal and relational data set consisting of millions of employment records from the National Association of Securities Dealers. The resulting tribes contain individuals at higher risk for fraud, are homogenous with respect to risk scores, and are geographically mobile, all at significant levels compared to random or to other sets of individuals who share affiliations.

Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic networks is important not only to understand life at the system-level, but also to discover novel patterns in other structural data. Most current graph-based data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. Our approach analyzes a sequence of graphs and discovers rules that capture the changes that occur between pairs of graphs in the sequence. These rules represent the graph rewrite rules that the first graph must go through to be isomorphic to the second graph. Then, our approach feeds the graph rewrite rules into a machine learning system that learns general transformation rules describing the types of changes that occur for a class of dynamic biological networks. The discovered graph-rewriting rules show how biological networks change over time, and the transformation rules show the repeated patterns in the structural changes. In this paper, we apply our approach to biological networks to evaluate our approach and to understand how the biosystems change over time. We evaluate our results using coverage and prediction metrics, and compare to biological literature.

Abstract: The analysis of social networks often assumes time invariant scenario, while in practice actor attributes and links in such networks often evolve over time and are inextricably dependent on each other. In this article, we propose a new method to predict actor attributes and links in temporal networks. Our approach takes into account the attributes corresponding to the participating actors together with topological and structural changes of the network over time. This is achieved by building two conditional predictors to jointly infer links and actor attributes. The proposed prediction method was significantly more accurate than alternatives when evaluated on synthetic data sets and two well-studied real-life temporal social networks. In addition, the new algorithm is computationally more efficient than a related alternative scaling up linearly with the number of temporal