Temporal Motif Participation Profiles for Analyzing Node Similarity in Temporal Networks

2025-07-08

AI Summary

This paper addresses the challenge of modeling node behavioral similarity in temporal networks. We propose Temporal Motif Participation Profiles (TMPPs), an interpretable, unsupervised node embedding method. TMPPs characterize high-order dynamic interaction roles by counting each node’s participation frequencies across positions in directed temporal triangle motifs—thereby transcending limitations of static or neighborhood-structure-dependent approaches. The method enables fine-grained role identification within short time windows and facilitates functional role alignment across heterogeneous neighbor structures. Experiments on synthetic networks and real-world international military conflict data demonstrate that TMPPs effectively identify semantically coherent, temporally similar node groups. Compared to baseline methods, TMPPs significantly enhance both the interpretability and practical utility of dynamic role discovery, offering principled insights into evolving node behaviors without supervision or domain-specific assumptions.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Social Networks and Social Media: Social mining and social search on the Web

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.

Problem

• Analyzing node similarity in temporal networks
• Capturing higher-order interaction patterns via temporal motifs
• Clustering nodes by roles using interpretable motif participation profiles

Innovation

• Temporal motif participation profiles for node similarity
• Unsupervised embeddings for interpretable node behavior
• Clustering TMPPs reveals similar node roles