Papers Hypergraph Contrastive Learning
“Hypergraph Contrastive Learning” 태그가 달린 논문 11편 · 필터 해제
BHyGNN+: Unsupervised Representation Learning for Heterophilic Hypergraphs
Hypergraph Neural Networks (HyGNNs) have demonstrated remarkable success in modeling higher-order relationships among entities. However, their performance often degrades on heterophilic hypergraphs, where nodes connected…
Hypergraph Contrastive LearningSelf-Supervised LearningRepresentation LearningDualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs
Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems. However, most existing methods rely on pairwise message passing mec…
Hypergraph Contrastive LearningRepresentation LearningKnowledge GraphsHypergraph Contrastive Learning for both Homophilic and Heterophilic Hypergraphs
Hypergraphs, as a generalization of traditional graphs, naturally capture high-order relationships. In recent years, hypergraph neural networks (HNNs) have been widely used to capture complex high-order relationships. Ho…
Hypergraph Contrastive LearningMMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation
The burgeoning presence of multimodal content-sharing platforms propels the development of personalized recommender systems. Previous works usually suffer from data sparsity and cold-start problems, and may fail to adequ…
Contrastive LearningHypergraph Contrastive LearningRecommendation SystemsPatch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer
Virtual stain transfer leverages computer-assisted technology to transform the histochemical staining patterns of tissue samples into other staining types. However, existing methods often lose detailed pathological infor…
Contrastive LearningHypergraph Contrastive LearningMulti-Channel Hypergraph Contrastive Learning for Matrix Completion
Rating is a typical user explicit feedback that visually reflects how much a user likes a related item. The (rating) matrix completion is essentially a rating prediction process, which is also a significant problem in re…
Contrastive LearningHypergraph Contrastive LearningMatrix CompletionRecommendation SystemsDual-level Hypergraph Contrastive Learning with Adaptive Temperature Enhancement
Inspired by the success of graph contrastive learning, researchers have begun exploring the benefits of contrastive learning over hypergraphs. However, these works have the following limitations in modeling the high-orde…
Contrastive LearningHypergraph Contrastive LearningHypergraph representationsRepresentation LearningFedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
In recent years, Cross-Domain Recommendation (CDR) has drawn significant attention, which utilizes user data from multiple domains to enhance the recommendation performance. However, current CDR methods require sharing u…
Contrastive LearningData AugmentationFederated LearningGraph Learning+5Hypergraph Contrastive Learning for Drug Trafficking Community Detection
In recent decades, due to the lucrative profits, the crime of drug trafficking has evolved with modern technologies. Social media, as one of the popular online platforms, have become direct-to-consumer intermediaries for…
AttributeCommunity DetectionContrastive LearningHypergraph Contrastive Learning+2Abnormal Event Detection via Hypergraph Contrastive Learning
Abnormal event detection, which refers to mining unusual interactions among involved entities, plays an important role in many real applications. Previous works mostly over-simplify this task as detecting abnormal pair-w…
Contrastive LearningEvent DetectionHypergraph Contrastive LearningAugmentations in Hypergraph Contrastive Learning: Fabricated and Generative
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on …
Contrastive LearningFairnessHypergraph Contrastive LearningRepresentation Learning