Tutorial 5: Anomalous and Significant Subgraph Detection in Attributed Networks

Detection of anomalous and significant subgraphs in attributed networks has applications in social networks, bioinformatics, disease surveillance and others. Different from vectors-space, single-vertex or whole graph versions, subgraph detection is often framed as a maximization of a score function over included node/edge attributes, where all connected or compact subgraphs are considered. Connectivity and compactness constraints ensure that subgraphs reflect changes due to localized in-network processes. The resulting problems are combinatorial in nature and, hence, require the design of efficient algorithms that scale to large real-world networks. In this tutorial, we will present a comprehensive review of the state-of-the-art methods for anomalous and significant subgraphs detection. First, we will classify popular score functions and structure constraints commonly used in the literature. Then we will review methods for static (planar, complex, and heterogeneous) and dynamic networks. We will illustrate the basic theoretical and algorithmic ideas and discuss specific applications in all the above settings.