74 citations · 189 across the 13 of their papers we have counts for
13 papers
ProWorld: Progress-Aware Hyperbolic World Models for Long-Horizon Visual Goal Reaching
Zihan Liu, Yuzhe Zhuang, Yuanzu Li +4
JEPA-style visual world models offer an effective paradigm for visual goal planning by predicting future latent representations. Existing methods typically learn local transition c…
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…
HICF: Hyperbolic Informative Collaborative Filtering
Menglin Yang, Zhihao Li, Min Zhou +2
Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the rec…
Boosting Factorization Machines via Saliency-Guided Mixup
Chenwang Wu, Defu Lian, Yong Ge +3
Factorization machines (FMs) are widely used in recommender systems due to their adaptability and ability to learn from sparse data. However, for the ubiquitous non-interactive fea…
HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization
Menglin Yang, Min Zhou, Jiahong Liu +2
In large-scale recommender systems, the user-item networks are generally scale-free or expand exponentially. The latent features (also known as embeddings) used to describe the use…
Discovering Representative Attribute-stars via Minimum Description Length
Jiahong Liu, Min Zhou, Philippe Fournier-Viger +3
Graphs are a popular data type found in many domains. Numerous techniques have been proposed to find interesting patterns in graphs to help understand the data and support decision…
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…
TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction
Min Zhou, Bisheng Li, Menglin Yang +1
Link prediction is a key problem for network-structured data, attracting considerable research efforts owing to its diverse applications. The current link prediction methods focus…
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…