A Sequential and Scalable Approach to Community Detection in Dynamic Graphs

We study a sequential sketch-based approach for the clustering of time-evolving graphs. We present a dynamic extension to the static Stochastic Block Model, which accommo- dates growing and shrinking graphs, as well as the movement of nodes between clusters. We then propose an online algorithm which constructs and maintains a small sketch consisting of nodes sampled from the full graph. The sketch is clustered and a retrieval algorithm is used to infer cluster membership of nodes in each successive graph snapshot. We demonstrate that the use of a small sketch not only improves computational complexity, but also improves the success rate when sketches are properly proportioned. We present a sampling method which chooses nodes according to node degree, whereby very small clusters can be successfully tracked.