6 citations · 7 across the 5 of their papers we have counts for
6 papers
SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Large scale pretrained models have revolutionized Natural Language Processing (NLP) and Computer Vision (CV), showcasing remarkable cross domain generalization abilities. However,…
Multi-Modal Molecular Representation Learning via Structure Awareness
Rong Yin, Ruyue Liu, Xiaoshuai Hao +4
Accurate extraction of molecular representations is a critical step in the drug discovery process. In recent years, significant progress has been made in molecular representation l…
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning
Ruyue Liu, Rong Yin, Yong Liu +4
Graph Contrastive Learning (GCL) has emerged as the foremost approach for self-supervised learning on graph-structured data. GCL reduces reliance on labeled data by learning robust…
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized…
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network
Ruyue Liu, Rong Yin, Yong Liu +1
Graph Comparative Learning (GCL) is a self-supervised method that combines the advantages of Graph Convolutional Networks (GCNs) and comparative learning, making it promising for l…
Triangle Counting Accelerations: From Algorithm to In-Memory Computing Architecture
Xueyan Wang, Jianlei Yang, Yinglin Zhao +5
Triangles are the basic substructure of networks and triangle counting (TC) has been a fundamental graph computing problem in numerous fields such as social network analysis. Never…