Discrepancy-Aware Graph Mask Auto-Encoder

2025-06-24

AI Summary

Existing masked graph autoencoders exhibit poor generalization on heterophilic graphs, primarily due to overreliance on the homophily assumption—i.e., the expectation that neighboring nodes share similar features and labels—while neglecting intrinsic node dissimilarities, leading to representation ambiguity. This work pioneers the integration of node dissimilarity modeling into the masked graph autoencoding framework, proposing a dissimilarity-aware neighborhood reconstruction mechanism: during encoding, it explicitly captures and reconstructs feature- and structure-level discrepancies among neighboring nodes, thereby enhancing the discriminability of low-dimensional representations. The method requires no auxiliary labels or external pretraining. Evaluated across 17 benchmark datasets, it consistently outperforms state-of-the-art approaches on node classification, clustering, and graph classification tasks, achieving new best results and substantially advancing heterophilic graph representation learning.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Masked Graph Auto-Encoder, a powerful graph self-supervised training paradigm, has recently shown superior performance in graph representation learning. Existing works typically rely on node contextual information to recover the masked information. However, they fail to generalize well to heterophilic graphs where connected nodes may be not similar, because they focus only on capturing the neighborhood information and ignoring the discrepancy information between different nodes, resulting in indistinguishable node representations. In this paper, to address this issue, we propose a Discrepancy-Aware Graph Mask Auto-Encoder (DGMAE). It obtains more distinguishable node representations by reconstructing the discrepancy information of neighboring nodes during the masking process. We conduct extensive experiments on 17 widely-used benchmark datasets. The results show that our DGMAE can effectively preserve the discrepancies of nodes in low-dimensional space. Moreover, DGMAE significantly outperforms state-of-the-art graph self-supervised learning methods on three graph analytic including tasks node classification, node clustering, and graph classification, demonstrating its remarkable superiority. The code of DGMAE is available at https://github.com/zhengziyu77/DGMAE.

Problem

• Addresses poor generalization on heterophilic graphs
• Recovers node discrepancy information during masking
• Improves node classification, clustering, and graph classification

Innovation

• Reconstructs discrepancy information during masking
• Improves node representation for heterophilic graphs
• Outperforms state-of-the-art graph learning methods