Abstract
A hypergraph allows a hyperedge to connect arbitrary number of vertices, which can be used to capture the complex and high-order relationships. By analyzing the iterative processing on bipartite graphs, a method of converting the original hypergraph into a hyperedge-connected graph and corresponding iterative processing method are proposed. Then, the iterative processing solution based on hyperedge-connected graphs is combined with Push-based and Pull-based message acquisition mechanisms. On top of the distributed graph processing system HybridGraph, a hypergraph iterative processing framework HyraphD is implemented. Finally, extensive experiments are conducted on several real-world datasets and hypergraph learning algorithms. Experimental results confirm the efficiency and the scalability of HyraphD.
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Acknowledgements
This work is supported by the National Nature Science Foundation of China (61872070, U1811261) and the Fundamental Research Funds or the Central Universities (N171605001).
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Yu, K., Gu, Y., Yao, S., Song, Z., Yu, G. (2019). Iterative Hypergraph Computation Based on Hyperedge-Connected Graphs. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_20
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DOI: https://doi.org/10.1007/978-3-030-26072-9_20
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