使用多種圖編碼進行鏈結預測

Network is a form of data representation, and it has been widely used in many fields. For example, in social networks, we regard nodes as individuals or groups, and the edges between nodes are called links, which means the interaction of the people. By analyzing the interaction of the nodes, we could learn more information on the relationship of the network. The core idea of link prediction is to predict whether there is a new relationship between the pair of nodes or to discover the hidden links in the network. Nowadays, link prediction has been used in social networks, e-commerce, biological information, and other fields. Moreover, researchers use graph embedding for link prediction, which effectively preserves the network structure and convert the node information into the lowdimensional vector space. In this study, we use three graph embedding methods: Matrix Factorization based methods, Random walk based methods, and Deep learning based methods. Each method has its own strength and weak, so we propose an ensemble model to combine these graph embedding to a new representation for each node. The new representations will be regarded as the input of our link prediction model. The performance evaluations will be conducted on multiple datasets. We expect that the obtained experimental results can show that using multiple graph embedding for representations can effectively improve the performance of link prediction.