Group Cohesion Assessment in Networks

Abstract

Networks measurement is essential to catch and quantify their features, behaviour and/or emerging phenomena. The goal of cohesiveness metric introduced here is to establish the level of cohesion among network nodes. It comes from the Black-Hole metric introduced as a solution of the normalization problem that affects PageRank; in particular, here we present an extension that leverages a set of black hole nodes to assess intra- and inter-group cohesion in partitioned networks. We carried out the evaluation in several real-world networks, also considering temporal dynamics and group size.

Marco Grassia

Assistant Professor · Network Science and Machine Learning

Assistant Professor. Researching Network Science and Geometric Deep Learning. University of Catania, Italy