Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors

2025-04-18

AI Summary

This paper identifies “excessive aggregation” as the root cause of oversmoothing in Graph Convolutional Networks (GCNs): higher-order neighborhood messages must propagate layer-by-layer through lower-order neighbors, leading to redundant and repeated information aggregation. To address this, we propose a redundancy-aware neighbor aggregation paradigm that abandons conventional deep stacking architectures. Specifically, we design a single-layer aggregator grounded in adjacency matrix power analysis and neighbor contribution modeling. Our approach eliminates the need for residual connections or stochastic edge dropout. Empirically, it significantly alleviates oversmoothing, improves performance of deep GCNs across multiple benchmark datasets, accelerates convergence, enhances generalization, and reduces model parameters by approximately 40%. The method is both structurally simple and highly scalable.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In recent years, Graph Convolutional Networks (GCNs) have gained popularity for their exceptional ability to process graph-structured data. Existing GCN-based approaches typically employ a shallow model architecture due to the over-smoothing phenomenon. Current approaches to mitigating over-smoothing primarily involve adding supplementary components to GCN architectures, such as residual connections and random edge-dropping strategies. However, these improvements toward deep GCNs have achieved only limited success. In this work, we analyze the intrinsic message passing mechanism of GCNs and identify a critical issue: messages originating from high-order neighbors must traverse through low-order neighbors to reach the target node. This repeated reliance on low-order neighbors leads to redundant information aggregation, a phenomenon we term over-aggregation. Our analysis demonstrates that over-aggregation not only introduces significant redundancy but also serves as the fundamental cause of over-smoothing in GCNs.

Problem

• Addresses redundancy in GCN message passing
• Identifies over-aggregation as cause of over-smoothing
• Proposes simplification of GCN neighbor interactions

Innovation

• Redundancy-free neighbors in GCNs
• Analyzing message passing mechanism
• Identifying over-aggregation as over-smoothing cause