How Does Topology Bias Distort Message Passing? A Dirichlet Energy Perspective

TL;DR

This paper analyzes how topology bias in graph message passing amplifies popularity bias in recommender systems, and proposes a higher-order simplicial complex method to mitigate this bias and improve long-tail item recommendations.

Contribution

It provides a Dirichlet energy-based analysis of topology bias in message passing and introduces Test-time Simplicial Propagation (TSP) to address this issue.

Findings

TSP effectively reduces topology bias.

TSP improves long-tail item recommendation.

Graph message passing inherently amplifies topology bias.

Abstract

Graph-based recommender systems have achieved remarkable effectiveness by modeling high-order interactions between users and items. However, such approaches are significantly undermined by popularity bias, which distorts the interaction graph's structure, referred to as topology bias. This leads to overrepresentation of popular items, thereby reinforcing biases and fairness issues through the user-system feedback loop. Despite attempts to study this effect, most prior work focuses on the embedding or gradient level bias, overlooking how topology bias fundamentally distorts the message passing process itself. We bridge this gap by providing an empirical and theoretical analysis from a Dirichlet energy perspective, revealing that graph message passing inherently amplifies topology bias and consistently benefits highly connected nodes. To address these limitations, we propose Test-time Simplicial Propagation (TSP), which extends message passing to higher-order simplicial complexes. By incorporating richer structures beyond pairwise connections, TSP mitigates harmful topology bias and substantially improves the representation and recommendation of long-tail items during inference. Extensive experiments across five real-world datasets demonstrate the superiority of our approach in mitigating topology bias and enhancing recommendation quality.