Search Results for author:
Found 2 papers, 1 papers with code
Graph Representation Learning Beyond Node and Homophily
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding.
Pair-view Unsupervised Graph Representation Learning
Low-dimension graph embeddings have proved extremely useful in various downstream tasks in large graphs, e. g., link-related content recommendation and node classification tasks, etc.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
