8 citations · 8 across the 5 of their papers we have counts for
6 papers
Scalable Subgraph Sampling via Resistance Curvature
Chaoqun Fei, Tinglve Zhou, Tianyong Hao +1
Subgraph sampling reduces the training cost of large-scale graph neural networks, but sampling criteria may overlook the geometric roles of edges. We propose a resistance-curvature…
Geometric Flow enhanced Graph Coarsening
Chaoqun Fei, Guoxuan Li, Tinglve Zhou +2
Recently, researchers have proposed a graph pooling operation, akin to the pooling process in conventional convolutional neural networks (CNN), aimed at reducing the computation co…
Logic Programming on Knowledge Graph Networks And its Application in Medical Domain
Chuanqing Wang, Zhenmin Zhao, Shanshan Du +3
The rash development of knowledge graph research has brought big driving force to its application in many areas, including the medicine and healthcare domain. However, we have foun…
Dynamic Graph Structure Learning via Resistance Curvature Flow
Chaoqun Fei, Huanjiang Liu, Tinglve Zhou +2
Geometric Representation Learning (GRL) aims to approximate the non-Euclidean topology of high-dimensional data through discrete graph structures, grounded in the manifold hypothes…
Efficient Curvature-aware Graph Network
Chaoqun Fei, Tinglve Zhou, Tianyong Hao +1
Graph curvature provides geometric priors for Graph Neural Networks (GNNs), enhancing their ability to model complex graph structures, particularly in terms of structural awareness…
Attributed Rhetorical Structure Grammar for Domain Text Summarization
Ruqian Lu, Shengluan Hou, Chuanqing Wang +3
This paper presents a new approach of automatic text summarization which combines domain oriented text analysis (DoTA) and rhetorical structure theory (RST) in a grammar form: the…