Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption

2025-12-17

AI Summary

To address the oversmoothing problem in unsupervised node representation learning on heterophilic graphs—where excessive neighborhood aggregation causes embedding collapse and intra-class confusion—this paper proposes an adaptive graph convolutional regulation framework. First, it dynamically modulates message-passing strength in an unsupervised setting, mitigating homogenization induced by neighborhood aggregation. Second, it introduces a feature-driven implicit clustering mechanism that discovers high-quality pseudo-classes without ground-truth labels. Third, it jointly optimizes intra-class compactness and inter-class separability by integrating contrastive learning with adaptive message passing. Extensive experiments across 14 benchmark datasets demonstrate significant improvements over 15 state-of-the-art baselines; notably, the method achieves new state-of-the-art performance on low-homophily graphs, with consistent gains in both node classification and clustering metrics.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Learning to Search
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Unsupervised node representation learning aims to obtain meaningful node embeddings without relying on node labels. To achieve this, graph convolution, which aggregates information from neighboring nodes, is commonly employed to encode node features and graph topology. However, excessive reliance on graph convolution can be suboptimal-especially in non-homophilic graphs-since it may yield unduly similar embeddings for nodes that differ in their features or topological properties. As a result, adjusting the degree of graph convolution usage has been actively explored in supervised learning settings, whereas such approaches remain underexplored in unsupervised scenarios. To tackle this, we propose FUEL, which adaptively learns the adequate degree of graph convolution usage by aiming to enhance intra-class similarity and inter-class separability in the embedding space. Since classes are unknown, FUEL leverages node features to identify node clusters and treats these clusters as proxies for classes. Through extensive experiments using 15 baseline methods and 14 benchmark datasets, we demonstrate the effectiveness of FUEL in downstream tasks, achieving state-of-the-art performance across graphs with diverse levels of homophily.

Problem

• Adaptively adjusts graph convolution usage for unsupervised node representation learning
• Enhances intra-class similarity and inter-class separability without homophily assumption
• Leverages node features to identify clusters as class proxies for diverse graphs

Innovation

• Adaptively learns graph convolution degree
• Uses node features as cluster proxies
• Enhances intra-class similarity and inter-class separability