Signed Directed Social Network Analysis Applied to Group Conflict

Abstract

Real-world social networks contain relationships of multiple different types, but this richness is often ignored in graph-theoretic modelling. We show how two recently developed spectral embedding techniques, for directed graphs (relationships are asymmetric) and for signed graphs (relationships are both positive and negative), can be combined. This combination is particularly appropriate for intelligence, terrorism, and law-enforcement applications. We illustrate by applying the novel embedding technique to datasets describing conflict in North-West Africa, and show how unusual interactions can be identified.