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• W3022206893 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.
• W3022206893 created "2020-05-13" @default.
• W3022206893 creator A5010923663 @default.
• W3022206893 creator A5011604061 @default.
• W3022206893 date "2020-05-11" @default.
• W3022206893 modified "2023-09-27" @default.
• W3022206893 title "Ring Reservoir Neural Networks for Graphs" @default.
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• W3022206893 hasPublicationYear "2020" @default.
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