Encoding Graph Topology with Randomized Ising Models

The increasing popularity of deep learning on graphs has motivated the need for the co-design of hardware and graph representation models. We propose Randomized Ising Model (RIM), a reservoir computing model for encoding topological information of graph nodes, that is amenable to physical implementation via neuromorphic hardware. Our experiments demonstrate that RIM’s node embeddings are able to provide sufficient topological information to be suitable to address node classification tasks, exhibiting an accuracy in line with Graph Echo State Networks.

Recommended citation: D. Tortorella, A. Brau, A. Micheli (2025). "Encoding Graph Topology with Randomized Ising Models." Proceedings of the 33rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2025), pp. 271-276.
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