Clustering Social Networks Using Competing Ant Hives

Methods for clustering static graphs cannot always be transferred straight forward to dynamic scenarios. A typical approach is to reduce the number of updates by reusing results of previous iterations. But are there natural ways to implement dynamic graph clustering? This paper proposes a method, which was derived by graph based ant colony algorithms. Similar to other clustering algorithms, multiple ant colonies are competing for the available nodes. Each hive creates ants, which will explore nearby graph structures and drop hive-specific pheromones on visited nodes. Over time, hives will collect nodes and will be relocated to the center of all collected nodes. In case of dynamic graph clustering, pheromone values can be reused in consecutive iterations. Our evaluation revealed that the proposed algorithm can lead to results on a par with the k-median algorithm and performs worse than Louvain clustering. However competing ant hives have the advantage of implicit noise detection, which comes at the cost of longer computation times. This can make it a suitable choice for certain clustering tasks.