Papers › Graph Classification using Structural Attention
Graph Classification using Structural Attention
John Boaz Lee, Ryan Rossi, Xiangnan Kong
Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire graph. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or subgraphs. In many real-world applications, however, graphs can be noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attention-based graph classification. The use of attention allows us to focus on small but informative parts of the graph, avoiding noise in the rest of the graph. We present a novel RNN model, called the Graph Attention Model (GAM), that processes only a portion of the graph by adaptively selecting a sequence of “informative” nodes. Experimental results on multiple real-world datasets show that the proposed method is competitive against various well-known methods in graph classification even though our method is limited to only a portion of the graph.
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 | HIV dataset | GAM | Accuracy | 74.79% | #5 of 5 | Archive leaderboard | report |
| Graph Classification | NCI-123 | GAM | Accuracy | 64.79% | #1 of 1 | Archive leaderboard | report |
| Graph Classification | NCI-83 | GAM | Accuracy | 70.42% | #1 of 1 | Archive leaderboard | report |
| Graph Classification | NCI1 | GAM | Accuracy | 67.71% | #65 of 69 | Archive leaderboard | report |
| Graph Classification | NCI33 | GAM | Accuracy | 69.58% | #1 of 1 | 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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