Papers › Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
Yi Luo, Guangchun Luo, Ke Yan, Aiguo Chen
Following the application of Deep Learning to graphic data, Graph Neural Networks (GNNs) have become the dominant method for Node Classification on graphs in recent years. To assign nodes with preset labels, most GNNs inherit the end-to-end way of Deep Learning in which node features are input to models while labels of pre-classified nodes are used for supervised learning. However, while these methods can make full use of node features and their associations, they treat labels separately and ignore the structural information of those labels. To utilize information on label structures, this paper proposes a method called 3ference that infers from references with differences. Specifically, 3ference predicts what label a node has according to the features of that node in concatenation with both features and labels of its relevant nodes. With the additional information on labels of relevant nodes, 3ference captures the transition pattern of labels between nodes, as subsequent analysis and visualization revealed. Experiments on a synthetic graph and seven real-world graphs proved that this knowledge about label associations helps 3ference to predict accurately with fewer parameters, fewer pre-classified nodes, and varying label patterns compared with GNNs.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Amazon Computers | 3ference | Accuracy | 90.74% | #8 of 12 | Archive leaderboard | report |
| Node Classification | Amazon Photo | 3ference | Accuracy | 95.05% | #7 of 11 | Archive leaderboard | report |
| Node Classification | Citeseer | 3ference | Accuracy | 76.33 | #12 of 71 | Archive leaderboard | report |
| Node Classification | Coauthor CS | 3ference | Accuracy | 95.99% | #4 of 24 | Archive leaderboard | report |
| Node Classification | Coauthor Physics | 3ference | Accuracy | 97.22% | #4 of 14 | Archive leaderboard | report |
| Node Classification | Cora | 3ference | Accuracy | 87.78% | #13 of 73 | Archive leaderboard | report |
| Node Classification | Pubmed | 3ference | Accuracy | 88.90 | #12 of 70 | 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.
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