Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
By Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Wei Qian. International Conference on Machine Learning, 2022
296 citations recorded by the source corpus.
We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily. However, it is a significant challenge to set personalized neighborhood sizes for different nodes. Further, for other homophilous nodes excluded in the neighborhood, they are ignored for information aggregation. To address these problems, we propose two models GloGNN and GloGNN++, which generate a node's embedding by aggregating information from global nodes in the graph. In each layer, both models learn a coefficient matrix to capture the correlations between nodes, based on which neighborhood aggregation is performed. The coefficient matrix allows signed values and is derived from an optimization problem that has a closed-form solution. We further accelerate neighborhood aggregation and derive a linear time complexity. We theoretically explain the models' effectiveness by proving that both the coefficient matrix and the generated node embedding matrix have the desired grouping effect. We conduct extensive experiments to compare our models against 11 other competitors on 15 benchmark datasets in a wide range of domains, scales and graph heterophilies. Experimental results show that our methods achieve superior performance and are also very efficient.
Subfields: Artificial Intelligence, Neurology
View Paper: https://www.semanticscholar.org/paper/b25b4d70b62d8482c98c2b901f4a7e1600df3a72
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sitao Luan
- Artem Babenko
- Doina Precup
- Mingde Zhao
- Michael Diskin
- Chenqing Hua
- Denis Kuznedelev
- Shuyuan Zhang
In Neurology
Citations in the corpus
- Graph Neural Networks for Graphs With Heterophily: A Survey (2022, unknown): “Typically, HOG-GCN [52] and GloGNN [53] design similarity calculation modules to capture the correlations between global nodes through both topological and attribute information.”
- A critical look at the evaluation of GNNs under heterophily: are we really making progress? (2023, methodology): “Heterophily-specific models We use eight models designed for node classification under het-erophily: H 2 GCN (Zhu et al., 2020), CPGNN (Zhu et al., 2021), GPR-GNN (Chien et al., 2021), FSGNN (Maurya et al., 2022), GloGNN (Li et al., 2022), FAGCN (Bo et al., 2021), GBK-8 GNN (Du et al., 2022), and Jaco”
- Revisiting Heterophily For Graph Neural Networks (2022, background): “Heterophily has received lots of attention recently and an increasing number of models have been put forward to address this problem [46, 29, 32, 8, 45, 44, 33, 16, 24].”