Papers › Factorizable Graph Convolutional Networks
Factorizable Graph Convolutional Networks
Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang
Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like friends and colleagues, are usually connected via a bare edge that conceals such intrinsic connections. In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph. FactorGCN takes a simple graph as input, and disentangles it into several factorized graphs, each of which represents a latent and disentangled relation among nodes. The features of the nodes are then aggregated separately in each factorized latent space to produce disentangled features, which further leads to better performances for downstream tasks. We evaluate the proposed FactorGCN both qualitatively and quantitatively on the synthetic and real-world datasets, and demonstrate that it yields truly encouraging results in terms of both disentangling and feature aggregation. Code is publicly available at https://github.com/ihollywhy/FactorGCN.PyTorch.
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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 | COLLAB | FactorGCN | Accuracy | 81.2% | #8 of 39 | Archive leaderboard | report |
| Graph Classification | COLLAB | FactorGCN | Accuracy (10-fold) | 81.2% | #8 of 39 | Archive leaderboard | report |
| Graph Classification | IMDb-B | FactorGCN | Accuracy | 75.3% | #24 of 51 | Archive leaderboard | report |
| Graph Classification | IMDb-B | FactorGCN | Accuracy (10-fold) | 75.3% | #24 of 51 | Archive leaderboard | report |
| Graph Classification | MUTAG | FactorGCN | Accuracy | 89.9% | #26 of 74 | Archive leaderboard | report |
| Graph Classification | MUTAG | FactorGCN | Accuracy (10-fold) | 89.9% | #26 of 74 | Archive leaderboard | report |
| Graph Regression | ZINC | FactorGCN | MAE | 0.366 | #26 of 27 | Archive leaderboard | report |
| Node Classification | PATTERN 100k | FactorGCN | Accuracy (%) | 86.57 ± 0.02 | #3 of 9 | 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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