33 citations · 69 across the 5 of their papers we have counts for
5 papers
Geometry Contrastive Learning on Heterogeneous Graphs
Shichao Zhu, Chuan Zhou, Anfeng Cheng +4
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data. Meanwhile, most existing r…
Ultrahyperbolic Knowledge Graph Embeddings
Bo Xiong, Shichao Zhu, Mojtaba Nayyeri +4
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-wor…
Multi-Graph Fusion Networks for Urban Region Embedding
Shangbin Wu, Xu Yan, Xiaoliang Fan +5
Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime predict…
Pseudo-Riemannian Graph Convolutional Networks
Bo Xiong, Shichao Zhu, Nico Potyka +3
Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry.…
Graph Geometry Interaction Learning
Shichao Zhu, Shirui Pan, Chuan Zhou +3
While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the…