Structured discovery in graphs: Recommender systems and temporal graph analysis

Abstract

Graph-valued data arises in numerous diverse scientific fields ranging from sociology, epidemiology and genomics to neuroscience and economics.For example, sociologists have used graphs to examine the roles of user attributes (gender, class, year) at American colleges and universities through the study of Facebook friendship networks and have studied segregation and homophily in social networks; epidemiologists have recently modeled Human-nCov protein-protein interactions via graphs, and neuroscientists have used graphs to model neuronal connectomes.

The structure of graphs, including latent features, relationships between the vertex and importance of each vertex are all highly important graph properties that are main aspects of graph analysis/inference. While it is common to imbue nodes and/or edges with implicitly observed numeric or qualitative features, in this work we will consider latent network features that must be estimated from the network topology.The main focus of this text is to find ways of extracting the latent structure in the presence of network anomalies. These anomalies occur in different scenarios: including cases when the graph is subject to an adversarial attack and the anomaly is inhibiting inference, and in the scenario when detecting the anomaly is the key inference task. The former case is explored in the context of vertex nomination information retrieval, where we consider both analytic methods for countering the adversarial noise and also the addition of a user-in-the-loop in the retrieval algorithm to counter potential adversarial noise. In the latter case we use graph embedding methods to discover sequential anomalies in network time series.