107 citations · 309 across the 14 of their papers we have counts for
14 papers
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…
WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering
Yankai Chen, Yifei Zhang, Menglin Yang +3
Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations,…
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…
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…
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Yankai Chen, Menglin Yang, Yingxue Zhang +4
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently…
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
Menglin Yang, Min Zhou, Marcus Kalander +2
Representation learning over temporal networks has drawn considerable attention in recent years. Efforts are mainly focused on modeling structural dependencies and temporal evolvin…
FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
Menglin Yang, Ziqiao Meng, Irwin King
Dynamic graphs arise in a plethora of practical scenarios such as social networks, communication networks, and financial transaction networks. Given a dynamic graph, it is fundamen…