Graph Representation Learning Enhancement Based on Local Optimization

With the successful application of graph representation learning in multiple fields, graph representation learning methods designed for different graph data and problems have exploded. However, the existence of graph noise limits the ability of graph representation learning. In order to effectively reduce the proportion of noise in the graph network, we first analyze the distribution characteristics of the local adjacency of the graph nodes, and theoretically prove that in the construction of the local adjacency topology, exploring high-order neighbor information can optimize the performance of the enhanced graph representation learning. Second, we propose “2-Steps” local subgraph optimization strategy (LOSO). This strategy first constructs a local adjacency similarity matrix with multi-order information based on the original graph topology information. Then, based on the similarity matrix and the local information of the graph nodes, the graph nodes are locally subgraph structure optimization. The proportion of noise in the network through the reasonable reconstruction of local adjacencies is reduced, and then the enhancement of graph representation learning ability is achieved. In the experiments of node classification, link prediction and community discovery tasks, the results indicate the local subgraph optimization strategy in this paper can boost the performance of 8 baseline algorithms. Among them, in the node classification tasks of the three aviation networks, the highest improvement effect reaches 23.11%, 41.58%, and 24.16%, respectively.