Papers › CensNet: Convolution with Edge-Node Switching in Graph Neural Networks
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks
Xiaodong Jiang, Pengsheng Ji, Sheng Li
In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach has achieved or matched the state-of-the-art performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Regression | Lipophilicity | CensNet | RMSE@80%Train | 0.93 | #21 of 23 | Archive leaderboard | report |
| Graph Regression | Lipophilicity | Logistic Regression | RMSE@80%Train | 1.15 | #22 of 23 | Archive leaderboard | report |
| Graph Regression | Lipophilicity | Random Forests | RMSE@80%Train | 1.16 | #23 of 23 | Archive leaderboard | report |
| Graph Regression | Tox21 | CensNet | AUC@80%Train | 0.78 | #1 of 3 | Archive leaderboard | report |
| Graph Regression | Tox21 | Random Forest | AUC@80%Train | 0.71 | #2 of 3 | Archive leaderboard | report |
| Graph Regression | Tox21 | Logistic Regression | AUC@80%Train | 0.71 | #3 of 3 | 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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