Community detection with node attributes in multilayer networks


Community detection in networks is commonly performed using information about interactions between nodes. Recent advances have been made to incorporate multiple types of interactions, thus generalising standard methods to multilayer networks. Often though, one can access additional information regarding individual nodes, attributes or covariates. A relevant question is thus how to properly incorporate this extra information in such frameworks. Here we develop a method that incorporates both the topology of interactions and node attributes to extract communities in multilayer networks. We propose a principled probabilistic method that does not assume any a priori correlation structure between attributes and communities but rather infers this from data. This leads to an efficient algorithmic implementation that exploits the sparsity of the dataset and can be used to perform several inference tasks; we provide an open-source implementation of the code online. We demonstrate our method on both synthetic and real-world data and compare performance with methods that do not use any attribute information. We find that including node information helps in predicting missing links or attributes. It also leads to more interpretable community structures and allows the quantification of the impact of the node attributes given in input.

Under review
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.