Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality

2025-01-06

AI Summary

To address the limited expressive power and high computational overhead of subgraph-based GNNs, this paper proposes HyMN—a unified framework that leverages random-walk-based centrality measures (e.g., PageRank, k-step centrality) for both subgraph sampling and structural encoding. Centrality scores guide the selection of informative subgraphs and serve as structural feature embeddings, thereby alleviating the expressivity bottlenecks of message-passing neural networks (MPNNs) and reducing redundant subgraph computations. Theoretical analysis via perturbation sensitivity demonstrates the robustness and effectiveness of centrality-guided sampling. HyMN adopts a lightweight hybrid architecture, achieving performance on par with full-subgraph GNNs and state-of-the-art models on both synthetic and real-world graph benchmarks, while significantly reducing runtime—thus balancing high discriminative power with computational efficiency.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We propose an expressive and efficient approach that combines the strengths of two prominent extensions of Graph Neural Networks (GNNs): Subgraph GNNs and Structural Encodings (SEs). Our approach leverages walk-based centrality measures, both as a powerful form of SE and also as a subgraph selection strategy for Subgraph GNNs. By drawing a connection to perturbation analysis, we highlight the effectiveness of centrality-based sampling, and show it significantly reduces the computational burden associated with Subgraph GNNs. Further, we combine our efficient Subgraph GNN with SEs derived from the calculated centrality and demonstrate this hybrid approach, dubbed HyMN, gains in discriminative power. HyMN effectively addresses the expressiveness limitations of Message Passing Neural Networks (MPNNs) while mitigating the computational costs of Subgraph GNNs. Through a series of experiments on synthetic and real-world tasks, we show it outperforms other subgraph sampling approaches while being competitive with full-bag Subgraph GNNs and other state-of-the-art approaches with a notably reduced runtime.

Problem

• Graph Information Processing
• Subgraph Neural Networks
• Computational Efficiency

Innovation

• Hybrid Method
• Subgraph Neural Networks
• Path Importance Scoring