Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length
Graph pooling compresses graphs and summarises their topological properties and features in a vectorial representation. It is an essential part of deep graph representation learning and is indispensable in graph-level ta…
Community DetectionGraph ClassificationGraph Representation LearningRepresentation LearningMolecular Graph Representation Learning via Structural Similarity Information
Graph Neural Networks (GNNs) have been widely employed for feature representation learning in molecular graphs. Therefore, it is crucial to enhance the expressiveness of feature representation to ensure the effectiveness…
Graph Representation Learningmolecular representationProperty PredictionRepresentation LearningMulti-object event graph representation learning for Video Question Answering
Video question answering (VideoQA) is a task to predict the correct answer to questions posed about a given video. The system must comprehend spatial and temporal relationships among objects extracted from videos to perf…
Contrastive LearningGraph Representation LearningObjectQuestion Answering+2GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning
Graph representation learning has emerged as a powerful tool for preserving graph topology when mapping nodes to vector representations, enabling various downstream tasks such as node classification and community detecti…
Community DetectionContrastive LearningGraph Neural NetworkGraph Representation Learning+2Graffin: Stand for Tails in Imbalanced Node Classification
Graph representation learning (GRL) models have succeeded in many scenarios. Real-world graphs have imbalanced distribution, such as node labels and degrees, which leaves a critical challenge to GRL. Imbalanced inputs ca…
ClassificationData AugmentationGraph Representation LearningNode Classification+1MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization
Electronic Design Automation (EDA) is essential for IC design and has recently benefited from AI-based techniques to improve efficiency. Logic synthesis, a key EDA stage, transforms high-level hardware descriptions into …
Graph ClassificationGraph Representation LearningMulti-Task LearningRepresentation LearningEthereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning
Ethereum faces growing fraud threats. Current fraud detection methods, whether employing graph neural networks or sequence models, fail to consider the semantic information and similarity patterns within transactions. Mo…
AttributeFraud DetectionGraph Representation LearningLanguage Modeling+4Debiasing Graph Representation Learning based on Information Bottleneck
Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make discriminatory predictions due to insuff…
Decision MakingFairnessGraph Representation LearningRepresentation LearningWhen Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning
Unsupervised heterogeneous graph representation learning (UHGRL) has gained increasing attention due to its significance in handling practical graphs without labels. However, heterophily has been largely ignored, despite…
Graph Representation LearningRepresentation LearningPSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System
A second-order-based latent factor (SLF) analysis model demonstrates superior performance in graph representation learning, particularly for high-dimensional and incomplete (HDI) interaction data, by incorporating the cu…
Graph Representation LearningRecommendation SystemsRepresentation LearningSiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration
Heterogeneous Graph Neural Networks (HGNNs) have expanded graph representation learning to heterogeneous graph fields. Recent studies have demonstrated their superior performance across various applications, including me…
Graph GenerationGraph Representation LearningRecommendation SystemsRepresentation Learning+1Neural Spacetimes for DAG Representation Learning
We propose a class of trainable deep learning-based geometries called Neural Spacetimes (NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) as events in a spacetime manifold. While mo…
Graph Representation LearningRepresentation LearningDisentangled Generative Graph Representation Learning
Recently, generative graph models have shown promising results in learning graph representations through self-supervised methods. However, most existing generative graph representation learning (GRL) approaches rely on r…
DisentanglementGraph LearningGraph Representation LearningRepresentation Learning+1Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks
Graph Neural Networks (GNNs) have become essential tools for graph representation learning in various domains, such as social media and healthcare. However, they often suffer from fairness issues due to inherent biases i…
AttributeDisentanglementFairnessGraph Representation Learning+1Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models
Molecular property prediction is a crucial foundation for drug discovery. In recent years, pre-trained deep learning models have been widely applied to this task. Some approaches that incorporate prior biological domain …
DescriptiveDrug DiscoveryGeneral KnowledgeGraph Representation Learning+3Dynamic Graph Representation Learning for Passenger Behavior Prediction
Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and timely risk management. This is crucial for …
Graph Representation LearningPredictionRepresentation LearningCEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning
Temporal knowledge graph reasoning (TKGR) is increasingly gaining attention for its ability to extrapolate new events from historical data, thereby enriching the inherently incomplete temporal knowledge graphs. Existing …
Graph Representation LearningKnowledge GraphsLink PredictionRepresentation LearningPath-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation
Unified graph representation learning aims to produce node embeddings, which can be applied to multiple downstream applications. However, existing studies based on graph neural networks and language models either suffer …
Graph LearningGraph Representation LearningLanguage ModelingLanguage Modelling+4Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning
Textual graphs are ubiquitous in real-world applications, featuring rich text information with complex relationships, which enables advanced research across various fields. Textual graph representation learning aims to g…
Graph LearningGraph Representation LearningLanguage ModellingLink Prediction+2Knowledge Probing for Graph Representation Learning
Graph learning methods have been extensively applied in diverse application areas. However, what kind of inherent graph properties e.g. graph proximity, graph structural information has been encoded into graph representa…
Graph ClassificationGraph LearningGraph Representation LearningKnowledge Probing+3