Fast Approximate Temporal Butterfly Counting on Bipartite Graphs via Edge Sampling

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Temporal bipartite graphs are widely used to represent time-evolving relationships between two disjoint sets of nodes, e.g., customer-product interactions in e-commerce and user-group memberships in social networks. Temporal butterflies, i.e., the complete bipartite subgraphs that occur between two nodes from each partition within a short period and in a prescribed order, are essential in modeling the structural and sequential patterns of such graphs. Counting the number of temporal butterflies is a fundamental task in temporal bipartite graph analysis. However, existing methods for butterfly counting on static bipartite graphs and motif counting on temporal unipartite graphs are inefficient for this purpose. Since exact counting can be time-consuming on large graphs, in this paper, we propose an edge sampling-based approach to approximating temporal butterfly counts accurately and efficiently. We provide an analytical bound on the number of edges to be sampled to obtain estimates with small relative errors and high probability. Finally, we evaluate our algorithm on six real-world temporal bipartite graphs to show its superior accuracy and efficiency compared to baselines.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications. DASFAA 2024 International Workshops - BDMS, GDMA, BDQM and ERDSE, 2024, Proceedings
EditorsAtsuyuki Morishima, Guoliang Li, Yoshiharu Ishikawa, Sihem Amer-Yahia, H.V. Jagadish, Kejing Lu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages42-57
Number of pages16
ISBN (Print)9789819609130
DOIs
StatePublished - 2025
Event10th International Workshop on Big Data Management and Service, BDMS 2024, 9th International Workshop on Big Data Quality Management, BDQM 2024, DASFAA 2024 Workshop on Emerging Results in Data Science and Engineering, ERDSE 2024 and 8th International Workshop on Graph Data Management and Analysis, GDMA 2024 held in conjunction with 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024 - Gifu, Japan
Duration: 2 Jul 20245 Jul 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14667 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Big Data Management and Service, BDMS 2024, 9th International Workshop on Big Data Quality Management, BDQM 2024, DASFAA 2024 Workshop on Emerging Results in Data Science and Engineering, ERDSE 2024 and 8th International Workshop on Graph Data Management and Analysis, GDMA 2024 held in conjunction with 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024
Country/TerritoryJapan
CityGifu
Period2/07/245/07/24

Keywords

  • Bipartite graph
  • Butterfly counting
  • Temporal motif

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