LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction

Graph neural networks lose a lot of their computing power when more network layers are added. As a result, the majority of existing graph neural networks have a shallow depth of learning. Over-smoothing and information loss are two of the key issues that restrict graph neural networks from going deeper. As network depth goes up, the embeddings of all the nodes eventually converge on the same value, which separates output representations from input vectors and causes over-smoothing. Moreover, layers of graph pooling are required in a deep learning model to retrieve specified features for prediction, which results in some degree of information loss. In this research, we present a new and multi-scale approach for overcoming these constraints by using concepts…