Multi-angle information aggregation for inductive temporal graph embedding.

ABSTRACT: Graph embedding has gained significant popularity due to its ability to represent large-scale graph data by mapping nodes to a low-dimensional space. However, most of the existing research in this field has focused on transductive learning, where fixed node embeddings are generated by training the entire graph. This approach is not well-suited for temporal graphs that undergo continuous changes with the addition of new nodes and interactions. To address this limitation, we propose an inductive temporal graph embedding method called MIAN (Multi-angle Information Aggregation Network). The key focus of MIAN is to design an aggregation function that combines multi-angle information for generating node embeddings. Specifically, we divide the information into different angles, including neighbor

SUBMITTER: Wei S 

PROVIDER: S-EPMC11623124 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature