Self-supervision meets kernel graph neural models: From architecture to augmentations
Self-supervision meets kernel graph neural models: From architecture to augmentations
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the most prevailing algorithmic tool. Despite its popularity, the family of MPNNs suffers from several drawbacks such as transparency and expressivity. Recently, the idea of designing …