Papers › RAN-GNNs: breaking the capacity limits of graph neural networks
RAN-GNNs: breaking the capacity limits of graph neural networks
Diego Valsesia, Giulia Fracastoro, Enrico Magli
Graph neural networks have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extracting better performance by increasing the number of layers. Recent works attribute this to a phenomenon peculiar to the extraction of node features in graph-based tasks, i.e., the need to consider multiple neighborhood sizes at the same time and adaptively tune them. In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks. Instead of building deeper networks by stacking many layers, we prove that employing a randomly-wired architecture can be a more effective way to increase the capacity of the network and obtain richer representations. We show that such architectures behave like an ensemble of paths, which are able to merge contributions from receptive fields of varied size. Moreover, these receptive fields can also be modulated to be wider or narrower through the trainable weights over the paths. We also provide extensive experimental evidence of the superior performance of randomly wired architectures over multiple tasks and four graph convolution definitions, using recent benchmarking frameworks that addresses the reliability of previous testing methodologies.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Property Prediction | ogbg-molpcba | RandomGIN-vn+FLAG | Ext. data | No | #22 of 36 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molpcba | RandomGIN-vn+FLAG | Number of params | 5572026 | #22 of 36 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molpcba | RandomGIN-vn+FLAG | Test AP | 0.2881 ± 0.0028 | #22 of 36 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molpcba | RandomGIN-vn+FLAG | Validation AP | 0.3035 ± 0.0047 | #22 of 36 | 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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