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Node Embedding Research Over Temporal Graph
Authors:Anbiao Wu  Ye Yuan  Yuliang M  Guoren Wang
Affiliation:School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;School of Business Administration, Northeastern University, Shenyang 110169, China
Abstract:Compared with conventional graph data analysis methods, the graph embedding algorithm provides a new graph data analysis strategy. It aims to encode graph nodes into vectors to mine or analyze graph data more effectively using neural network related technologies. Some classic tasks have been improved significantly by graph embedding methods, such as node classification, link prediction, and traffic flow prediction. Although substantial breakthroughs have been made by former researchers in graph embedding, the nodes embedding problem over temporal graph has been seldom studied. In this study, we propose an adaptive temporal graph embedding (ATGED), attempting to encode temporal graph nodes into vectors by combining previous research and the information propagation characteristics. First, an adaptive cluster method is proposed by solving the situation that nodes active frequency varies types of graph. Then, a new node walk strategy is designed in order to store the time sequence between nodes, and also the walking list will be stored in a bidirectional multi-tree in the walking process to get complete walking lists fast. Last, based on the basic walking characteristics and graph topology, an important node sampling strategy is proposed to train the satisfied neural network as soon as possible. Sufficient experiments demonstrate that the proposed method surpasses existing embedding methods in terms of node clustering, reachability prediction, and node classification in temporal graphs.
Keywords:temporal graph   node embedding   importance sampling   temporal reachability   node classification
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