95 citations · 135 across the 5 of their papers we have counts for
5 papers · 1 filter
Graph Coarsening with Neural Networks
Chen Cai, Dingkang Wang, Yusu Wang
As large-scale graphs become increasingly more prevalent, it poses significant computational challenges to process, extract and analyze large graph data. Graph coarsening is one po…
A Note on Over-Smoothing for Graph Neural Networks
Chen Cai, Yusu Wang
Graph Neural Networks (GNNs) have achieved a lot of success on graph-structured data. However, it is observed that the performance of graph neural networks does not improve as the…
Understanding the Power of Persistence Pairing via Permutation Test
Chen Cai, Yusu Wang
Recently many efforts have been made to incorporate persistence diagrams, one of the major tools in topological data analysis (TDA), into machine learning pipelines. To better unde…
Group Representation Theory for Knowledge Graph Embedding
Chen Cai
Knowledge graph embedding has recently become a popular way to model relations and infer missing links. In this paper, we present a group theoretical perspective of knowledge graph…
Network Representation Learning: Consolidation and Renewed Bearing
Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan +9
Graphs are a natural abstraction for many problems where nodes represent entities and edges represent a relationship across entities. An important area of research that has emerged…