Papers › Transfer Entropy in Graph Convolutional Neural Networks
Transfer Entropy in Graph Convolutional Neural Networks
Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Graph Convolutional Networks (GCN) are Graph Neural Networks where the convolutions are applied over a graph. In contrast to Convolutional Neural Networks, GCN's are designed to perform inference on graphs, where the number of nodes can vary, and the nodes are unordered. In this study, we address two important challenges related to GCNs: i) oversmoothing; and ii) the utilization of node relational properties (i.e., heterophily and homophily). Oversmoothing is the degradation of the discriminative capacity of nodes as a result of repeated aggregations. Heterophily is the tendency for nodes of different classes to connect, whereas homophily is the tendency of similar nodes to connect. We propose a new strategy for addressing these challenges in GCNs based on Transfer Entropy (TE), which measures of the amount of directed transfer of information between two time varying nodes. Our findings indicate that using node heterophily and degree information as a node selection mechanism, along with feature-based TE calculations, enhances accuracy across various GCN models. Our model can be easily modified to improve classification accuracy of a GCN model. As a trade off, this performance boost comes with a significant computational overhead when the TE is computed for many graph nodes.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Actor | TE-GCNN | Accuracy | 37.50±1.57 | #22 of 62 | Archive leaderboard | report |
| Node Classification | Chameleon | TE-GCNN | Accuracy | 71.14 ± 1.84 | #27 of 61 | Archive leaderboard | report |
| Node Classification | Cornell | TE-GCNN | Accuracy | 85.68 ± 6.63 | #21 of 60 | Archive leaderboard | report |
| Node Classification | Squirrel | TE-GCNN | Accuracy | 55.04±1.64 | #38 of 59 | Archive leaderboard | report |
| Node Classification | Texas | TE-GCNN | Accuracy | 84.86 ± 4.55 | #33 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | TE-GCNN | Accuracy | 87.45 ± 3.70 | #29 of 63 | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections