Papers Hypergraph representations
“Hypergraph representations” 태그가 달린 논문 11편 · 필터 해제
Higher-order Structure Boosts Link Prediction on Temporal Graphs
Temporal Graph Neural Networks (TGNNs) have gained growing attention for modeling and predicting structures in temporal graphs. However, existing TGNNs primarily focus on pairwise interactions while overlooking higher-or…
Dynamic Link PredictionGraph LearningGraph Neural NetworkHypergraph representations+1Hypergraph Representations of scRNA-seq Data for Improved Clustering with Random Walks
Analysis of single-cell RNA sequencing data is often conducted through network projections such as coexpression networks, primarily due to the abundant availability of network analysis tools for downstream tasks. However…
Hypergraph representationsEvolving Skeletons: Motion Dynamics in Action Recognition
Skeleton-based action recognition has gained significant attention for its ability to efficiently represent spatiotemporal information in a lightweight format. Most existing approaches use graph-based models to process s…
Action RecognitionHypergraph representationsSkeleton Based Action RecognitionLLaSA: Large Language and Structured Data Assistant
Structured data, such as tables, graphs, and databases, play a critical role in plentiful NLP tasks such as question answering and dialogue system. Recently, inspired by Vision-Language Models, Graph Neutral Networks (GN…
Hypergraph representationsQuestion AnsweringSelf-Supervised LearningDual-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 LearningHypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Hypergraphs are marked by complex topology, expressing higher-order interactions among multiple nodes with hyperedges, and better capturing the topology is essential for effective representation learning. Recent advances…
Hypergraph representationsNode ClassificationRepresentation LearningSelf-Supervised LearningHypergraph 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+2Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
Hypergraphs can naturally model group-wise relations (e.g., a group of users who co-purchase an item) as hyperedges. Hyperedge prediction is to predict future or unobserved hyperedges, which is a fundamental task in many…
Contrastive LearningHyperedge PredictionHypergraph representationsPrediction+1Synthetic Text Generation using Hypergraph Representations
Generating synthetic variants of a document is often posed as text-to-text transformation. We propose an alternate LLM based method that first decomposes a document into semantic frames and then generates text using this…
Hypergraph representationsText GenerationMessage Passing Neural Networks for Hypergraphs
Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing c…
Graph Neural NetworkHypergraph representationsNode ClassificationHNHN: Hypergraph Networks with Hyperedge Neurons
Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolution network with nonlinear activation fu…
Hypergraph representationsRepresentation Learning