Pyramidal Reservoir Graph Neural Network
This work addresses computational efficiency for graph learning tasks, presenting an incremental improvement by integrating existing techniques in a novel architecture.
The authors tackled the problem of computational complexity in Graph Neural Networks (GNNs) by proposing a pyramidal model that alternates Reservoir Computing layers with graph pooling, achieving an advantageous trade-off between accuracy and complexity as demonstrated on multiple graph datasets.
We propose a deep Graph Neural Network (GNN) model that alternates two types of layers. The first type is inspired by Reservoir Computing (RC) and generates new vertex features by iterating a non-linear map until it converges to a fixed point. The second type of layer implements graph pooling operations, that gradually reduce the support graph and the vertex features, and further improve the computational efficiency of the RC-based GNN. The architecture is, therefore, pyramidal. In the last layer, the features of the remaining vertices are combined into a single vector, which represents the graph embedding. Through a mathematical derivation introduced in this paper, we show formally how graph pooling can reduce the computational complexity of the model and speed-up the convergence of the dynamical updates of the vertex features. Our proposed approach to the design of RC-based GNNs offers an advantageous and principled trade-off between accuracy and complexity, which we extensively demonstrate in experiments on a large set of graph datasets.