2023/10/18 by Jiaxi Pu, Pu, Jiaxi, Yanhao Wang +5 · 1 citation
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Data Management and Algorithms #Data Structures and Algorithms (cs.DS) #Databases (cs.DB) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2310.11886
openalex publication_date 2023/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Temporal bipartite graphs are widely used to denote time-evolving relationships between two disjoint sets of nodes, such as customer-product interactions in E-commerce and user-group memberships in social networks. Temporal butterflies, (2,2)-bicliques that occur 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 thus a fundamental task in analyzing temporal bipartite graphs. However, existing algorithms for butterfly counting on static bipartite graphs and motif counting on temporal unipartite graphs are inefficient for this purpose. In this paper, we present a general framework with three sampling strategies for temporal butterfly counting. Since exact counting can be time-consuming on large graphs, our approach alternatively computes approximate estimates accurately and efficiently. We also provide analytical bounds on the number of samples each strategy requires to obtain estimates with small relative errors and high probability. We finally evaluate our framework on six real-world datasets and demonstrate its superior accuracy and efficiency compared to several baselines. Overall, our proposed framework and sampling strategies provide efficient and accurate approaches to approximating temporal butterfly counts on large-scale temporal bipartite graphs.