Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2020

Abstract

A great variety of complex systems ranging from user interactions in communication networks to transactions in financial markets can be modeled as temporal graphs, which consist of a set of vertices and a series of timestamped and directed edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs which take into account edge orderings and durations in addition to structures. Counting the number of occurrences of temporal motifs is a fundamental problem for temporal network analysis. However, existing methods either cannot support temporal motifs or suffer from performance issues. In this paper, we focus on approximate temporal motif counting via random sampling. We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif. Furthermore, we devise an improved EWS algorithm that hybridizes edge sampling with wedge sampling for counting temporal motifs with 3 vertices and 3 edges. We provide comprehensive analyses of the theoretical bounds and complexities of our proposed algorithms. Finally, we conduct extensive experiments on several real-world datasets, and the results show that our ES and EWS algorithms have higher efficiency, better accuracy, and greater scalability than the state-of-the-art sampling method for temporal motif counting.

Keywords

Temporal networks, Motif counting, Random sampling

Discipline

Numerical Analysis and Scientific Computing | Theory and Algorithms

Publication

CIKM '20: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Virtual, October 19-23

First Page

1505

Last Page

1514

ISBN

9781450368599

Identifier

10.1145/3340531.3411862

Publisher

ACM

City or Country

New York

Embargo Period

5-13-2021

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3340531.3411862

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