A Force-Directed Layout for Community Detection with Automatic Clusterization

Abstract

We present a force-directed layout algorithm to detect community structure within a network. Our algorithm places nodes in a two-dimensional grid and continuously updates their positions based on two opposing forces: nodes pull connected nodes closer and push non-connected nodes away. To compute the strength of the forces between nodes we make use of two insightful community properties: high-degree nodes contribute more inter-community edges than low-degree nodes and the graph-distance between two nodes is inversely proportional to the probability that they belong to the same community. We present empirical evidence in support of both of these claims. In conjunction, we use a clustering algorithm to monitor and interpret the current community structure. Running our algorithm on well-known social networks, we find that we produce accurate results that avoid some common pitfalls of alternative approaches.