Link Prediction Based on the Sub-graphs Learning with Fused Features

As one of the important research methods in the area of the knowledge graph completion, link prediction aims to capture the structural information or the attribute information of nodes in the network to predict the link probability between nodes, In particular, the graph neural networks based on the sub-graphs provide a popular approach for the learning representation to the link prediction tasks. However, they cannot solve the resource consumption in large graphs, nor do they combine global structural features since they often simply stitch attribute features and embedding to predict. Therefore, this paper proposes a novel link prediction model based on the Sub-graphs Learning with the Fused Features, named SLFF in short. In particular, the proposed model utilizes random walks to extract the sub-graphs to reduce the overhead in the process. Moreover, it utilizes the Node2Vec to process the entire graph and obtain the global structure characteristics of the node. Afterward, the SLFF model utilizes the existing embedding to reconstruct the embedding according to the neighborhood defined by the graph structure and node attribute space. Finally, the SLFF model can combine the attribute characteristics of the node with the structural characteristics of the node together. The extensive experiments on datasets demonstrates that the proposed SLFF has better performance than that of the state-of-the-art approaches.