Temporal Motif Participation Profiles for Analyzing Node Similarity in Temporal Networks

TL;DR

This paper introduces temporal motif participation profiles (TMPPs), a novel method for characterizing node roles in temporal networks by capturing their participation in higher-order interaction patterns over time.

Contribution

The paper proposes TMPPs as interpretable, unsupervised embeddings that reveal node roles based on temporal motif participation, enhancing analysis of evolving networks.

Findings

TMPPs effectively cluster nodes with similar roles.

Application to dispute networks uncovers meaningful role groupings.

TMPPs provide interpretable insights into node behavior.

Abstract

Temporal networks consisting of timestamped interactions between a set of nodes provide a useful representation for analyzing complex networked systems that evolve over time. Beyond pairwise interactions between nodes, temporal motifs capture patterns of higher-order interactions such as directed triangles over short time periods. We propose temporal motif participation profiles (TMPPs) to capture the behavior of nodes in temporal motifs. Two nodes with similar TMPPs take similar positions within temporal motifs, possibly with different nodes. TMPPs serve as unsupervised embeddings for nodes in temporal networks that are directly interpretable, as each entry denotes the frequency at which a node participates in a particular position in a specific temporal motif. We demonstrate that clustering TMPPs reveals groups of nodes with similar roles in a temporal network through simulation experiments and a case study on a network of militarized interstate disputes.