Reservoir-Based Graph Convolutional Networks
Message passing is a core mechanism in Graph Neural Networks (GNNs), enabling the iterative update of node embeddings by aggregating information from neighboring nodes. Graph Convolutional Networks (GCNs) exemplify this approach by adapting convolutional operations for graph structures, allowing features from adjacent nodes to be combined effectively. However, GCNs encounter challenges with complex or dynamic data. Capturing long-range dependencies often requires deeper layers, which not only increase computational costs but also lead to over-smoothing, where node embeddings become indistinguishable. To overcome these challenges, reservoir computing has been integrated into GNNs, leveragi...
| Authors | Mayssa Soussia, Gita Ayu Salsabila, Mohamed Ali Mahjoub et al. |
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| Primary category | cs.LG |
| Published | 25 March 2026 |
| Last updated at source | 4 September 2026 |
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