A Spark Based Scalable Framework for Efficient Hypergraph Processing
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src/main/scala/org/apache/spark/hyperx ready to produce experimental results Nov 28, 2014
.gitignore added idea into gitignore Nov 2, 2014
README.md Update README.md Jul 20, 2015
spark-hyperx.iml added the laplacian matrix implementation, but lack essential debugging Nov 7, 2014



A scalable framework for hypergraph processing and learning algorithms. HyperX is built upon Apache Spark and inspired by its graph counterpart, GraphX.

When processing a hypergraph (where an edge contains arbitrary number of vertices), instead of converting the hypergraph to a bipartite and employing GraphX to do the tricks, HyperX directly operates on a distributed hypergraph representation. By carefully optimizing the hypergraph partitioning strategies, the preliminary exprimental results show that HyperX is able to achieve a 49 speedup factor on the hypergraph random walks upon the bipartite GraphX solution.

A paper describing the details is now under review for ICDM 2015. A technical report can be found at http://iojin.com/resources/hyperx_report.pdf.