Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks

2026-09-06

AI Summary

该研究针对图神经网络中的回音室效应问题，提出了一种新的度量方法Echo Chamber Index，并设计了Community-Aware Split Propagation机制来改善这一问题。

Abstract

Oversmoothing is a well-known failure mode of Graph Neural Networks (GNNs). However, most existing diagnostics rely on global aggregation measures that fail to capture the heterogeneous dynamics of message passing. Real-world graphs exhibit pronounced community structure, and message passing operates on two timescales, with representations collapsing rapidly within communities and slowly across them. This creates a critical gap in which intra-community representations can become indistinguishable while inter community separation persists, a failure mode that we refer to as the Echo Chamber Effect. To quantify this effect, we introduce the Echo Chamber Index (ECI), which stratifies pairwise distances by community membership and reveals when global energy diminishes while inter-community separation persists. ECI further shows that feature retention mechanisms can preserve the echo chamber under the conditions of our theoretical analysis. The consequences depend on label structure: when communities align with classes, the echo chamber can sharpen node classification, whereas when they do not, the same collapse makes classification provably harder. Motivated by this analysis, we propose Community-Aware Split Propagation (CASP), a lightweight plugin that decouples intra- and inter-community aggregation and learns their balance from label structure. CASP improves diverse backbone GNNs across most evaluated homophilic and heterophilic settings.

Problem

• Echo Chamber Effect
• Graph Neural Networks
• Community Structure
• Message Passing
• Node Classification

Innovation

• Echo Chamber Effect
• Echo Chamber Index (ECI)
• Community-Aware Split Propagation (CASP)