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• W3090264346 abstract "Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highlighting the need for more efficient solutions. The class of Reservoir Computing (RC) models can play an important role in this context, enabling to develop fruitful graph embeddings through untrained recursive architectures. In this paper, we study progressive simplifications to the design strategy of RC neural networks for graphs. Our core proposal is based on shaping the organization of the hidden neurons to follow a ring topology. Experimental results on graph classification tasks indicate that ring-reservoirs architectures enable particularly effective network configurations, showing consistent advantages in terms of predictive performance." @default.
• W3090264346 created "2020-10-08" @default.
• W3090264346 creator A5010923663 @default.
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• W3090264346 date "2020-07-01" @default.
• W3090264346 modified "2023-10-16" @default.
• W3090264346 title "Ring Reservoir Neural Networks for Graphs" @default.
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• W3090264346 doi "https://doi.org/10.1109/ijcnn48605.2020.9206723" @default.
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