Graph Echo State Networks (GESN) have recently proved effective in node classification tasks, showing particularly able to address the issue of heterophily. While previous literature has analyzed the design of reservoirs for sequence ESN and GESN for graph-level tasks, the factors that contribute to rich node embeddings are so far unexplored. In this paper we analyze the impact of different reservoir designs on node classification accuracy and on the quality of node embeddings computed by GESN using tools from the areas of information theory and numerical analysis. In particular, we propose an entropy measure for quantifying information in node embeddings.
Richness of Node Embeddings in Graph Echo State Networks
Tortorella, Domenico;Micheli, Alessio
2023-01-01
Abstract
Graph Echo State Networks (GESN) have recently proved effective in node classification tasks, showing particularly able to address the issue of heterophily. While previous literature has analyzed the design of reservoirs for sequence ESN and GESN for graph-level tasks, the factors that contribute to rich node embeddings are so far unexplored. In this paper we analyze the impact of different reservoir designs on node classification accuracy and on the quality of node embeddings computed by GESN using tools from the areas of information theory and numerical analysis. In particular, we propose an entropy measure for quantifying information in node embeddings.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
