Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification
Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationshi…
BenchmarkingGraph Representation LearningRepresentation LearningTime Series+1CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and Prediction
The early detection and prediction of health status decline among the elderly at the neighborhood level are of great significance for urban planning and public health policymaking. While existing studies affirm the conne…
Graph Representation LearningRepresentation LearningOptimizing Supply Chain Networks with the Power of Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data, offering unprecedented capabilities in tasks like forecasting and optimization. This study investigates the applicat…
Demand ForecastingGraph Representation LearningManagementRepresentation Learning+1KAN KAN Buff Signed Graph Neural Networks?
Graph Representation Learning aims to create effective embeddings for nodes and edges that encapsulate their features and relationships. Graph Neural Networks (GNNs) leverage neural networks to model complex graph struct…
Community DetectionGraph Representation LearningLink Sign PredictionRepresentation LearningData-Driven Self-Supervised Graph Representation Learning
Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on…
Data AugmentationDocument ClassificationGraph Property PredictionGraph Representation Learning+3NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning
Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream learning tasks. For homogeneous graphs, the …
Graph LearningGraph Neural NetworkGraph Representation LearningRepresentation LearningLine Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning
While message passing graph neural networks result in informative node embeddings, they may suffer from describing the topological properties of graphs. To this end, node filtration has been widely used as an attempt to …
Graph ClassificationGraph Representation LearningRepresentation LearningLASE: Learned Adjacency Spectral Embeddings
We put forth a principled design of a neural architecture to learn nodal Adjacency Spectral Embeddings (ASE) from graph inputs. By bringing to bear the gradient descent (GD) method and leveraging the principle of algorit…
Graph AttentionGraph Neural NetworkGraph Representation LearningLink Prediction+2A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
Recent advancements in graph representation learning have shifted attention towards dynamic graphs, which exhibit evolving topologies and features over time. The increased use of such graphs creates a paramount need for …
Anomaly DetectionData AugmentationGraph GenerationGraph Representation Learning+2GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning
Graph contrastive learning (GCL) has become a hot topic in the field of graph representation learning. In contrast to traditional supervised learning relying on a large number of labels, GCL exploits augmentation strateg…
Contrastive LearningGraph Representation LearningRepresentation LearningScam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain
As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, st…
Fraud DetectionGraph Representation LearningRepresentation LearningRobust classificationA Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
Dynamic graph embedding has emerged as an important technique for modeling complex time-evolving networks across diverse domains. While transformer-based models have shown promise in capturing long-range dependencies in …
Computational EfficiencyDynamic graph embeddingGraph EmbeddingGraph Representation Learning+4Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction
Link Prediction is a foundational task in Graph Representation Learning, supporting applications like link recommendation, knowledge graph completion and graph generation. Graph Neural Networks have shown the most promis…
Graph GenerationGraph Representation LearningKnowledge Graph CompletionLink Prediction+2RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction
Organic Solar Cells (OSCs) are a promising technology for sustainable energy production. However, the identification of molecules with desired OSC properties typically involves laborious experimental research. To acceler…
Graph Representation LearningMolecular Property PredictionProperty PredictionRepresentation LearningRepository-Level Graph Representation Learning for Enhanced Security Patch Detection
Software vendors often silently release security patches without providing sufficient advisories (e.g., Common Vulnerabilities and Exposures) or delayed updates via resources (e.g., National Vulnerability Database). Ther…
graph constructionGraph Representation LearningRepresentation LearningMixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through self-attention, yet face scalability and …
Computational EfficiencyGraph Representation LearningMixture-of-Experts+3Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?
Graph contrastive learning (GCL) has been widely used as an effective self-supervised learning method for graph representation learning. However, how to apply adequate and stable graph augmentation to generating proper v…
Contrastive LearningGraph Representation LearningRepresentation LearningSelf-Supervised LearningBootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach
Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges wh…
Graph Representation LearningRepresentation LearningFine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent…
Contrastive LearningGraph Representation LearningGraph structure learningKnowledge Graphs+2A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disea…
Graph Representation LearningRepresentation Learning