GraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs

GNNs are widely used to solve various tasks including node classification and link prediction. Most of the GNN architectures assume the initial embedding to be random or generated from popular distributions. These initial embeddings require multiple layers of transformation to converge into a meaningful latent representation. While number of layers allow accumulation of larger neighbourhood of a node it also introduce the problem of over-smoothing. In addition, GNNs are inept at representing structural information. For example, the output embedding of …

architectures classification converge cs.ai cs.lg cs.si embedding embeddings feature generated gnn gnns link prediction multiple node popular prediction random representation solve tasks transformation