14 citations · 19 across the 18 of their papers we have counts for
6 papers · 1 filter
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
Yunhui Liu, Qizhuo Xie, Jinwei Shi +2
Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespre…
Negative-Free Self-Supervised Gaussian Embedding of Graphs
Yunhui Liu, Tieke He, Tao Zheng +1
Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The w…
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference
Yunhui Liu, Xinyi Gao, Tieke He +2
Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate rel…
Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering
Yunhui Liu, Tieke He, Qing Wu +2
Attributed graph clustering, which aims to group the nodes of an attributed graph into disjoint clusters, has made promising advancements in recent years. However, most existing me…
Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
Yunhui Liu, Huaisong Zhang, Tieke He +2
Contrastive learning is a significant paradigm in graph self-supervised learning. However, it requires negative samples to prevent model collapse and learn discriminative represent…
Reliable Node Similarity Matrix Guided Contrastive Graph Clustering
Yunhui Liu, Xinyi Gao, Tieke He +3
Graph clustering, which involves the partitioning of nodes within a graph into disjoint clusters, holds significant importance for numerous subsequent applications. Recently, contr…