Influence-Based Community Deception

Abstract

This paper studies the novel problem of influence-based community deception. Tackling this problem amounts to devising tools to protect the users of a community from being discovered by community detection algorithms. The novel setting considers networks that have both edge directions and models the influence of nodes as edge weights. We present a deception strategy based on modularity. We conducted an experimental evaluation that shows the feasibility of our proposal.