Parallel Triangle Counting in Massive Streaming Graphs

被引:26
作者
Tangwongsan, Kanat [1 ]
Pavan, A. [2 ]
Tirthapura, Srikanta [2 ]
机构
[1] IBM Res, Yorktown Hts, NY 10598 USA
[2] Iowa State Univ, Ames, IA USA
来源
PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13) | 2013年
基金
美国国家科学基金会;
关键词
triangle counting; streaming algorithm; parallel algorithm; parallel cache oblivious (PCO); massive graphs;
D O I
10.1145/2505515.2505741
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The number of triangles in a graph is a fundamental metric widely used in social network analysis, link classification and recommendation, and more. In these applications, modern graphs of interest tend to both large and dynamic. This paper presents the design and implementation of a fast parallel algorithm for estimating the number of triangles in a massive undirected graph whose edges arrive as a stream. Our algorithm is designed for shared-memory multicore machines and can make efficient use of parallelism and the memory hierarchy. We provide theoretical guarantees on performance and accuracy, and our experiments on real-world datasets show accurate results and substantial speedups compared to an optimized sequential implementation.
引用
收藏
页码:781 / 786
页数:6
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