Discovery of Temporal Network Motifs

Hanqing Chen, Shuai Ma, Junfeng Liu, Lizhen Cui

Abstract: Network motifs provide a deep insight into the network functional abilities, and have proven useful in various practical applications. Existing studies reveal that different definitions of motifs may be needed for different temporal networks. In this study, we focus on a class of temporal networks such that the nodes and edges keep fixed, but the edge labels vary regularly with timestamps. First, we propose a proper definition of temporal motifs, which appear continuously within sufficiently large time intervals, to properly reinterpret the recurrent and statistically significant nature of motifs in temporal networks. Second, we develop a low polynomial time solution to find temporal motifs for all possible time intervals with the top to bottom and right to left scheme, based on the analyses of the properties for temporal motifs. Third, we develop a theoretically faster incremental solution to efficiently find temporal motifs to support continuously updates of temporal networks, by identifying unaffected time intervals and unnecessary edges. Finally, we have conducted extensive experiments to verify the efficiency and usefulness of our static and incremental solutions.

External IDs:dblp:journals/tkde/ChenMLC25