74 citations · 189 across the 13 of their papers we have counts for
7 papers · 1 filter
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Tianmeng Yang, Min Zhou, Yujing Wang +4
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many res…
Hyperbolic Representation Learning: Revisiting and Advancing
Menglin Yang, Min Zhou, Rex Ying +2
The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable attention in the realm of representation learning. Current endeavors in hyperbolic representation…
kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning
Menglin Yang, Min Zhou, Lujia Pan +1
The prevalence of tree-like structures, encompassing hierarchical structures and power law distributions, exists extensively in real-world applications, including recommendation sy…
Hyperbolic Graph Representation Learning: A Tutorial
Min Zhou, Menglin Yang, Lujia Pan +1
Graph-structured data are widespread in real-world applications, such as social networks, recommender systems, knowledge graphs, chemical molecules etc. Despite the success of Eucl…
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction
Bisheng Li, Min Zhou, Shengzhong Zhang +3
Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph c…
Hyperbolic Graph Neural Networks: A Review of Methods and Applications
Menglin Yang, Min Zhou, Tong Zhang +5
Graph representation learning in Euclidean space, despite its widespread adoption and proven utility in many domains, often struggles to effectively capture the inherent hierarchic…
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning
Jiahong Liu, Menglin Yang, Min Zhou +2
Recently, hyperbolic space has risen as a promising alternative for semi-supervised graph representation learning. Many efforts have been made to design hyperbolic versions of neur…