Fast Parallel Tensor Times Same Vector for Hypergraphs

Hypergraphs are a popular paradigm to represent complex real-world networks exhibiting multi-way relationships of varying sizes. Mining centrality in hypergraphs via symmetric adjacency tensors has only recently become computationally feasible for large and complex datasets. To enable scalable compu...

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Bibliographic Details
Main Authors Shivakumar, Shruti, Amburg, Ilya, Aksoy, Sinan G, Li, Jiajia, Young, Stephen J, Aluru, Srinivas
Format Journal Article
LanguageEnglish
Published 14.11.2023
Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Computer Science - Numerical Analysis
Mathematics - Combinatorics
Mathematics - Numerical Analysis
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Summary:Hypergraphs are a popular paradigm to represent complex real-world networks exhibiting multi-way relationships of varying sizes. Mining centrality in hypergraphs via symmetric adjacency tensors has only recently become computationally feasible for large and complex datasets. To enable scalable computation of these and related hypergraph analytics, here we focus on the Sparse Symmetric Tensor Times Same Vector (S$^3$TTVc) operation. We introduce the Compound Compressed Sparse Symmetric (CCSS) format, an extension of the compact CSS format for hypergraphs of varying hyperedge sizes and present a shared-memory parallel algorithm to compute S$^3$TTVc. We experimentally show S$^3$TTVc computation using the CCSS format achieves better performance than the naive baseline, and is subsequently more performant for hypergraph $H$-eigenvector centrality.
DOI:10.48550/arxiv.2311.08595