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Incremental Graph Clustering in Parallel
DescriptionWe develop a distributed memory graph clustering algorithm to find clusters in a graph where new nodes and edges are being added incrementally. At each stage of the algorithm, we maintain a summary of the clustered graph computed from all incremental batches received thus far. As we receive a new batch of nodes and edges, we cluster the new graph and merge new clusters with the previous summary clusters. We use sparse linear algebra to perform these operations. Our algorithm would make it possible to find clusters in very large graphs for which regular graph clustering algorithms could not run due to computation/communication bottlenecks.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Research Posters
Scientific Visualization & Data Analytics Showcase
TimeTuesday, 14 November 20235:15pm - 7pm MST
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