2026-04-13 ノースカロライナ州立大学（NC State）
Image credit: BoliviaInteligente.
＜関連情報＞
- https://news.ncsu.edu/2026/04/improving-graph-neural-network-accuracy/
- https://openreview.net/forum?id=mGxtoQY3GA
HarmonyGNNs: 自己教師ありノードエンコーディングによるGNNにおける異質性と同質性の調和 HarmonyGNNs: Harmonizing Heterophily and Homophily in GNNs via Self-Supervised Node Encoding
Rui Xue, Tianfu Wu
the Fourteenth International Conference on Learning Representations(ICLR2026) Published: 26 Jan 2026
Abstract
Graph Neural Networks (GNNs) have made significant advances in representation learning on various types of graph-structured data. However, GNNs struggle to simultaneously model heterophily and homophily, a challenge that is amplified under self-supervised learning (SSL) where no labels are available to guide the training process. This paper presents HarmonyGNNs, an end-to-end graph SSL framework designed to harmonize heterophily and homophily through two complementary innovative perspectives: (i) Representation Harmonization via Joint Structural Node Encoding. Nodes are embedded into a unified latent space that retains both node specificity and graph structural awareness for harmonizing heterophily and homophily. Node specificity is learned via linear and non-linear node feature projections. Graph structural awareness is learned via a proposed Weighted Graph Convolutional Network (WGCN). A self-attention module enables the model learning-to-adapt to varying levels of patterns. (ii) Objective Harmonization via Predictive Architecture with Node-Difficulty–Aware Masking. A teacher network processes the full graph. A student network receives a partially masked graph. The student is trained end-to-end, while the teacher is an exponential moving average of the student. The proxy task is to train the student to predict the teacher’s embeddings for all nodes (masked and unmasked). To keep the objective informative across the graph, two masking strategies that guide selection toward currently hard nodes while retaining exploration are proposed. Theoretical underpinnings of HarmonyGNNs are also analyzed in detail. Comprehensive evaluations on benchmarks demonstrate that HarmonyGNNs achieves state-of-the-art performance on heterophilic graphs (e.g., +7.1% on Texas, +9.6% on Roman-Empire over the prior art) while matching SOTA on homophilic graphs, and delivering strong computational efficiency.
