Jason Riedy is presenting work using the STINGER framework to track communities at rates of up to 100 million updates per second at the 7th Workshop on Multithreaded Architectures and Applications (MTAAP) in Boston. The approach re-agglomerates around only the portions of the graph that change to minimize unneeded computation.
Abstract—Analyzing static snapshots of massive, graph-structured data cannot keep pace with the growth of social networks, ﬁnancial transactions, and other valuable data sources. Current state-ofthe-art industrial methods analyze these streaming sources using only simple, aggregate metrics. There are few existing scalable algorithms for monitoring complex global quantities like decomposition into community structure. Using our framework STING, we present the ﬁrst known parallel algorithm speciﬁcally for monitoring communities in this massive, streaming, graph-structured data. Our algorithm performs incremental re-agglomeration rather than starting from scratch after each batch of changes, reducing the problem’s size to that of the change rather than the entire graph. We analyze our initial implementation’s performance on multithreaded platforms for execution time and latency. On an Intel-based multithreaded platform, our algorithm handles up to 100 million updates per second on social networks with one to 30 million edges, providing a speed-up from 4× to 3700× over statically recomputing the decomposition after each batch of changes. Possibly because of our artiﬁcial graph generator, resulting communities’ modularity varies little from the initial graph.
The full paper is available here.
J. Riedy and D.A. Bader, "Multithreaded Community Monitoring for Massive Streaming Graph Data," 7th Workshop on Multithreaded Architectures and Applications (MTAAP), Boston, MA, May 24, 2013.