Embedding Global and Local Influences for Dynamic Graphs

Graph embedding is becoming increasingly popular due to its ability of representing large-scale graph data by mapping nodes to low-dimensional space. Current research usually focuses on transductive learning, which aims to generates fixed node embeddings by training the whole graph. However, dynamic graph changes constantly with new node additions and interactions. Unlike transductive learning, inductive learning attempts to dynamically generate node embeddings over time even for unseen nodes, which is more suitable for real-world applications. Therefore, we propose an inductive dynamic graph embedding method called AGLI by aggregating global and local influences. We propose an aggregator function that integrates global influence with local influence to generate node embeddings at any time. We conduct extensive experiments on several real-world datasets and compare AGLI with several state-of-the-art baseline methods on various tasks. The experimental results show that AGLI achieves better performance than the state-of-the-art baseline methods.