Communities in Streaming Graphs: Small Space Data Structure, Benchmark Data Generation, and Linear Algorithm

Identifying and preserving community structures in a streaming graph is a very challenging task. However, many applications require the identification of these communities in very limited space and time. In this paper, we design Community Sketch, a small space data  structure that efficiently preserves communities. On query, it provides communities in constant time. With the use of community sketch data structure, a linear streaming community detection algorithm is proposed. Experimental results on the large real-world networks show that our algorithm outperforms other state-of-the-art algorithms in terms of quality metrics (NMI, F1-score, and WCC). Further, we propose an algorithm to produce benchmark network, namely, Temporal Community Benchmark Dataset (TCBD) which contains both true community labels and temporal information of edges. These synthetic networks are used to validate the proposed algorithm