Journal article

HyperX: A Scalable Hypergraph Framework

Wenkai Jiang, Jianzhong Qi, Jeffrey Xu Yu, Jin Huang, Rui Zhang

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | IEEE COMPUTER SOC | Published : 2019

Abstract

Hypergraphs are generalizations of graphs where the (hyper)edges can connect any number of vertices. They are powerful tools for representing complex and non-pairwise relationships. However, existing graph computation frameworks cannot accommodate hypergraphs without converting them into graphs, because they do not offer APIs that support (hyper)edges directly. This graph conversion may create excessive replicas and result in very large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a general-purpose distributed hypergraph ..

View full abstract

Grants

Awarded by Australian Research Council (ARC)


Awarded by ARC


Awarded by Research Grants Council of the Hong Kong SAR, China


Funding Acknowledgements

This work is supported by the Australian Research Council (ARC) Discovery Project DP180102050, ARC Future Fellowships Projects FT120100832, and the Research Grants Council of the Hong Kong SAR, China, No. 14221716. The work was done when J. Huang was with the School of Computing and Information Systems, The University of Melbourne, Australia.