Papers › DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Jun Wu, Jingrui He, Jiejun Xu
Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn powerful node-level or graph-level representation. However, most of the existing graph neural networks suffer from the following limitations: (1) there is limited analysis regarding the graph convolution properties, such as seed-oriented, degree-aware and order-free; (2) the node's degree-specific graph structure is not explicitly expressed in graph convolution for distinguishing structure-aware node neighborhoods; (3) the theoretical explanation regarding the graph-level pooling schemes is unclear. To address these problems, we propose a generic degree-specific graph neural network named DEMO-Net motivated by Weisfeiler-Lehman graph isomorphism test that recursively identifies 1-hop neighborhood structures. In order to explicitly capture the graph topology integrated with node attributes, we argue that graph convolution should have three properties: seed-oriented, degree-aware, order-free. To this end, we propose multi-task graph convolution where each task represents node representation learning for nodes with a specific degree value, thus leading to preserving the degree-specific graph structure. In particular, we design two multi-task learning methods: degree-specific weight and hashing functions for graph convolution. In addition, we propose a novel graph-level pooling/readout scheme for learning graph representation provably lying in a degree-specific Hilbert kernel space. The experimental results on several node and graph classification benchmark data sets demonstrate the effectiveness and efficiency of our proposed DEMO-Net over state-of-the-art graph neural network models.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | ENZYMES | DEMO-Net(weight) | Accuracy | 27.2 | #51 of 54 | Archive leaderboard | report |
| Node Classification | BlogCatalog | DEMO-Net(weight) | Accuracy | 84.9 | #2 of 6 | Archive leaderboard | report |
| Node Classification | Brazil Air-Traffic | DEMO-Net(weight) | Accuracy | 0.543 ± 0.034 | #2 of 7 | Archive leaderboard | report |
| Node Classification | Europe Air-Traffic | DEMO-Net(weight) | Accuracy | 45.9 | #3 of 7 | Archive leaderboard | report |
| Node Classification | DEMO-Net(weight) | Accuracy | 91.9 | #4 of 8 | Archive leaderboard | report | |
| Node Classification | Flickr | DEMO-Net(weight) | Accuracy | 0.656 ± 0.000 | #2 of 8 | Archive leaderboard | report |
| Node Classification | USA Air-Traffic | DEMO-Net(weight) | Accuracy | 64.7 | #2 of 7 | Archive leaderboard | report |
| Node Classification | Wiki-Vote | DEMO-Net(weight) | Accuracy | 99.8 | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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