Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering

2025-06-03

AI Summary

To address the challenge of modeling higher-order, dynamic, and overlapping multi-body interactions in temporal hypergraphs, this paper proposes an unsupervised pattern discovery method based on hyperedge clustering. Unlike conventional node-centric clustering, our approach defines a time-aware structural similarity measure in the hyperedge space—featuring three scalable design variants—and integrates spectral clustering with hyperedge-space embedding to automatically identify dense interaction subpatterns. This work is the first to extend the edge-clustering paradigm to temporal hypergraphs, overcoming inherent limitations of node-based modeling in representing higher-order structural dependencies. Experiments on large-scale collaborative hypergraphs demonstrate that the discovered patterns exhibit strong semantic coherence and interpretability, and effectively support downstream tasks such as collaboration prediction and role discovery.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by clustering the edges of the graph and then assigning a vertex to a community if it has at least one edge in that community, thereby allowing for overlapping clusters of vertices. We apply the idea behind edge clustering to temporal hypergraphs, an extension of a graph where a single edge can contain any number of vertices and each edge has a timestamp. Extending to hypergraphs allows for many different patterns of interaction between edges, and by defining a suitable structural similarity function, our edge clustering algorithm can find clusters of these patterns. We test the algorithm with three structural similarity functions on a large collaboration hypergraph, and find intuitive cluster structures that could prove useful for downstream tasks.

Problem

• Detecting densely connected vertex subsets in temporal hypergraphs
• Clustering edges to identify overlapping vertex communities
• Analyzing interaction patterns via structural similarity functions

Innovation

• Edge clustering for temporal hypergraphs
• Structural similarity functions for pattern detection
• Overlapping vertex communities via edge assignment