AUTHOR=Grasso Roberto , Micale Giovanni , Ferro Alfredo , Pulvirenti Alfredo TITLE=MODIT: MOtif DIscovery in Temporal Networks JOURNAL=Frontiers in Big Data VOLUME=4 YEAR=2022 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2021.806014 DOI=10.3389/fdata.2021.806014 ISSN=2624-909X ABSTRACT=

Temporal networks are graphs where each edge is linked with a timestamp, denoting when an interaction between two nodes happens. According to the most recently proposed definitions of the problem, motif search in temporal networks consists in finding and counting all connected temporal graphs Q (called motifs) occurring in a larger temporal network T, such that matched target edges follow the same chronological order imposed by edges in Q. In the last few years, several algorithms have been proposed to solve motif search, but most of them are limited to very small or specific motifs due to the computational complexity of the problem. In this paper, we present MODIT (MOtif DIscovery in Temporal Networks), an algorithm for counting motifs of any size in temporal networks, inspired by a very recent algorithm for subgraph isomorphism in temporal networks, called TemporalRI. Experiments show that for big motifs (more than 3 nodes and 3 edges) MODIT can efficiently retrieve them in reasonable time (up to few hours) in many networks of medium and large size and outperforms state-of-the art algorithms.