Exploration of Asymmetric Relationships in Graph Neural Network Topology

As an information extraction framework, graph neural network has been widely used in many fields, but there is a serious problem of over-smoothing in graph neural network, that is, with the increase of the number of iterations, the nodes gradually tending toward similarity, resulting in reduced feature distinguishability and deteriorated model performance. In order to solve this problem, this paper proposes a novel solution, that is, to alleviate the over-smoothing problem in graph neural networks from the perspective of changing the topological information dimension. This article uses graph regularization to solve this problem, by fine-tuning the topology structure, the research objectives can be achieved, and demonstrated through extensive ablation experiments, a large number of experiments verify the effectiveness and feasibility of the proposed method. Physically speaking, this method limits the connection strength of the adjacency matrix to a finite number of steps; The higher the order, the more obvious the restriction effect, therefore, it can alleviate the problem of over smoothing in graph neural networks to a certain extent.