Abstract
Graph Masked AutoEncoder (GMAE) has recently attracted vast interest in handling graph-related tasks by adopting the 'masking-reconstruction' learning paradigm. Most existing GMAE-based methods adhere to the homophily assumption, i.e., connected nodes share the same attributes or labels. However, this assumption is not always right because most graphs from real-world applications are mixed by both homophilic and heterophilic edges. Therefore, it is necessary to distinguish them to improve the representative ability of GMAE. In this paper, we propose a heterophily-aware personalized graph masked autoencoder (HAP-GMAE). Specifically, we design a teacher-guided edge discriminator that distinguishes homophilic and heterophilic edges by leveraging the embeddings from teacher models with structure and attribute knowledge. Then, we present a personalized graph masked autoencoder that individually tailors the masking, encoding, and reconstruction processes for each graph. Finally, we optimize the model by minimizing two types of loss functions, i.e., the scaled cosine error (SCE) loss and the InfoNCE loss. Experimental results on 10 datasets demonstrate the superior performance of HAP-GMAE on the tasks of node classification and node clustering.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
"Heterophily-Aware Personalized Graph Masked Autoencoder", Technical Disclosure Commons, (May 04, 2025)
https://www.tdcommons.org/dpubs_series/8084