Graph Models

Graph Echo State Network

Graph Echo State Network (GraphESN) model is a generalization of the Echo State Network (ESN) approach to graph domains. GraphESNs allow for an efficient approach to Recursive Neural Networks (RecNNs) modeling extended to deal with cyclic/acyclic, directed/undirected, labeled graphs. The recurrent reservoir of the network computes a fixed contractive encoding function over graphs and is left untrained after initialization, while a feed-forward readout implements an adaptive linear output function. Contractivity of the state transition function implies a Markovian characterization of state dynamics and stability of the state computation in presence of cycles. Due to the use of fixed (untrained) encoding, the model represents both an extremely efficient version and a baseline for the performance of recursive models with trained connections.

Description from: Graph Echo State Networks


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