A PageRank Inspired Approach to Measure Network Cohesiveness

Abstract

Basics of PageRank algorithm have been widely adopted in its variations, tailored for specific scenarios. In this work, we consider the Black Hole metric, an extension of the original PageRank that leverages a (bogus) black hole node to reduce the arc weights normalization effect. We further extend this approach by introducing several black holes to investigate on the cohesiveness of the network, a measure of the strength among nodes belonging to the network. First experiments on real networks show the effectiveness of the proposed approach.

Marco Grassia

Assistant Professor · Network Science and Machine Learning

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