Anomaly, reciprocity, and community detection in networks

Abstract

Anomaly detection algorithms are a valuable tool in network science for identifying unusual patterns in a network. These algorithms have numerous practical applications, including detecting fraud, identifying network security threats, and uncovering significant interactions within a dataset. In this project, we propose a probabilistic generative approach that incorporates community membership and reciprocity as key factors driving regular behavior in a network, which can be used to identify potential anomalies that deviate from expected patterns. We model pairs of edges in a network with exact two-edge joint distributions. As a result, our approach captures the exact relationship between pairs of edges and provides a more comprehensive view of social networks. Additionally, our study highlights the role of reciprocity in network analysis and can inform the design of future models and algorithms. We also develop an efficient algorithmic implementation that takes advantage of the sparsity of the network.

Publication
Physical Review Research, 5, 033084
Hadiseh Safdari
Hadiseh Safdari
Postdoctoral researcher

My current research revolves around inference and modeling in networks. More precisely, we aim to relax the independence assumptions in generative models by deploying hidden variables, and establishing analytical approximations to make the inference problem tractable.

Martina Contisciani
Martina Contisciani
PhD student

My research focuses on the analysis of network data using statistical tools. My background is in Theoretical and Applied Statistics and I am interested in discovering new techniques, approaches and perspectives used in the analysis of data. I have been working on a project focused on modeling covariate information in community detection algorithms and I am involved in investigating the conditional independence assumption, underlying the statistical inference on network data.

Caterina De Bacco
Caterina De Bacco
CyberValley Research Group Leader

My research focuses on understanding, optimizing and predicting relations between the microscopic and macroscopic properties of complex large-scale interacting systems.

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