Randomized Greedy Modularity Optimization for Group Detection in Huge Social Networks

Due to the increasing availability of very large data sets of social networks, there is a need for scalable algorithms that are able to analyze these networks with reasonable resource requirements. Finding ’natural groups’ in these networks has gotten much attention lately. We present an algorithm that detects communities by optimizing the modularity, which is a measure for the quality of a given decomposition of a network into clusters. As the calculation of the clustering with maximal modularity is NP-hard, various heuristic algorithms have been proposed. Still, the time or memory complexity of those algorithms does not allow finding communities in huge networks. In this article we present a memory-efficient randomized greedy algorithm to gain a speed-up compared to the state-of-the-art while the achieved cluster quality remains very high. The algorithm is compared to the previously best algorithms on 9 publicly available data sets and shown to be of comparable performance while being faster and using considerably less memory.