Year: 2025
Type: preprint
Abstract: Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downstream performance. However, most existing graph neural networks (GNNs) rely on message passing, which aggregates local neighborhood information iteratively and struggles to explicitly capture such fundamental motifs, like triangles, k-cliques, and rings. This limitation hinders both expressiveness and long-range dependency modeling. In this paper, we introduce the Neural Graph Pattern Machine (GPM), a novel framework that bypasses message passing by learning directly from graph substructures. GPM efficiently extracts, encodes, and prioritizes task-relevant graph patterns, offering greater expressivity and improved ability to capture long-range dependencies. Empirical evaluations across four standard tasks -- node classification, link prediction, graph classification, and graph regression -- demonstrate that GPM outperforms state-of-the-art baselines. Further analysis reveals that GPM exhibits strong out-of-distribution generalization, desirable scalability, and enhanced interpretability. Code and datasets are available at: https://github.com/Zehong-Wang/GPM.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: III: Small: A New Machine Learning Paradigm Towards Effective yet Efficient Foundation Graph Learning Models, SCC-IRG Track 1: A New AI-driven Paradigm to Promote Community Resilience for Teenagers and Young Adults in Preventing Opioid Misuse and Addiction, CAREER: Taming Networks in the Wild: A Safety-Centric Network Learning Framework, III: Medium: A Data-driven and AI-augmented Framework for Collaborative Decision Making to Combat Infectious Disease Outbreaks, EAGER: A New Explainable Multi-objective Learning Framework for Personalized Dietary Recommendations against Opioid Misuse and Addiction, D-ISN: An AI-augmented Framework to Detect, Disrupt, and Dismantle Opioid Trafficking Networks
OpenAlex ID: W4407092991