Efficient processing of streaming graphs for evolution-aware clustering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we propose techniques for clustering large-scale "streaming" graphs where the updates to a graph are given in form of a stream of vertex or edge additions and deletions. Our algorithm handles such updates in an online and incremental manner and it can be easily parallel zed. Several previous graph clustering algorithms fall short of handling massive and streaming graphs because they are centralized, they need to know the entire graph beforehand and are not incremental, or they incur an excessive computational overhead. Our algorithm's fundamental building block is called graph reservoir sampling. We maintain a reservoir sample of the edges as the graph changes while satisfying certain desired properties like bounding number of clusters or cluster-sizes. We then declare connected components in the sampled sub graph as clusters of the original graph. Our experiments on real graphs show that our approach not only yields clusterings with very good quality, but also obtains orders of magnitude higher throughput, when compared to offline algorithms.