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Top-k heavy weight triangles listing on graph stream

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Abstract

Graph stream is used to express complex and highly dynamic relationships between entities, such as friendships in social networks. The storage and mining on graph stream are the main research areas of big data research. Triangle listing/counting is an important topic in graph mining research. The triangle, as the simplest circle and clique structure, has many applications in many real-world scenarios. A large amount of related work also exists on the study of triangles on graph streams. However, the existing research has focused on triangle counting on static graphs or graph streams, and there is a lack of research targeting heavy weight triangle listing. This paper formally defines the triangle weight on graph stream. Based on this definition, this paper presents an approximation algorithm for the top-k heavy weight triangle listing problem on graph stream, and proposes various optimized data structures DolhaT, Filtered DolhaT and Double Filtered DolhaT (DFD) to solve this problem. Experiments on real graph stream datasets demonstrate the effectiveness of the proposed optimized structures for the heavy weight triangle listing problem on graph stream.

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Authors and Affiliations

  1. WICT, Peking University, Zhongguancun North Street, Haidian District, 100871, Beijing, China

    Fan Zhang, Xiangyang Gou & Lei Zou

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  1. Fan Zhang
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  2. Xiangyang Gou
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  3. Lei Zou
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Contributions

Fan Zhang was a major contributor in the problem definition and the algorithm design and implementation as well as the manuscript writing. Xiangyang Gou revised the paper writing. Lei Zou guided the whole project and revised the paper writing. All authors read and approved the final manuscript.

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Correspondence to Lei Zou.

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Zhang, F., Gou, X. & Zou, L. Top-k heavy weight triangles listing on graph stream. World Wide Web 26, 1827–1851 (2023). https://doi.org/10.1007/s11280-022-01117-z

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