Search Results for author:
Found 6 papers, 4 papers with code
Geometry Contrastive Learning on Heterogeneous Graphs
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.
Ultrahyperbolic Knowledge Graph Embeddings
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.
Multi-View Substructure Learning for Drug-Drug Interaction Prediction
Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment.
Multi-Graph Fusion Networks for Urban Region Embedding
Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
Pseudo-Riemannian Graph Convolutional Networks
Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
Graph Geometry Interaction Learning
To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.
