Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs
The modelling of temporal patterns in dynamic graphs is an important current research issue in the development of time-aware GNNs. Whether or not a specific sequence of events in a temporal graph constitutes a temporal p…
Graph Representation LearningInductive BiasNode ClassificationRepresentation LearningGraph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease
Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical statistical and machine learning methods. Graph neural networks have emerged as promising al…
Graph AttentionGraph Representation LearningRepresentation LearningHarvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments
The recent past has witnessed a notable surge in on-demand food delivery (OFD) services, offering delivery fulfillment within dozens of minutes after an order is placed. In OFD, pooling multiple orders for simultaneous d…
Graph Representation LearningNetwork EmbeddingRepresentation LearningEffective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs
Graph representation learning (GRL) is to encode graph elements into informative vector representations, which can be used in downstream tasks for analyzing graph-structured data and has seen extensive applications in va…
AttributeEdge ClassificationGraph Representation LearningRepresentation LearningRobGC: Towards Robust Graph Condensation
Graph neural networks (GNNs) have attracted widespread attention for their impressive capability of graph representation learning. However, the increasing prevalence of large-scale graphs presents a significant challenge…
DenoisingGraph Representation LearningRepresentation LearningA Scalable and Effective Alternative to Graph Transformers
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph…
Graph LearningGraph Representation LearningRepresentation LearningA Unified Graph Selective Prompt Learning for Graph Neural Networks
In recent years, graph prompt learning/tuning has garnered increasing attention in adapting pre-trained models for graph representation learning. As a kind of universal graph prompt learning method, Graph Prompt Feature …
Graph Representation LearningPrompt LearningRepresentation LearningOLGA: One-cLass Graph Autoencoder
One-class learning (OCL) comprises a set of techniques applied when real-world problems have a single class of interest. The usual procedure for OCL is learning a hypersphere that comprises instances of this class and, i…
Graph Representation LearningRepresentation LearningIntroducing Diminutive Causal Structure into Graph Representation Learning
When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable challenge for the model in accurately cap…
Graph Representation LearningRepresentation LearningPredicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification
Predicting genetic mutations from whole slide images is indispensable for cancer diagnosis. However, existing work training multiple binary classification models faces two challenges: (a) Training multiple binary classif…
Binary ClassificationGraph Representation LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Learning Long Range Dependencies on Graphs via Random Walks
Message-passing graph neural networks (GNNs) excel at capturing local relationships but struggle with long-range dependencies in graphs. In contrast, graph transformers (GTs) enable global information exchange but often …
Graph ClassificationGraph Property PredictionGraph RegressionGraph Representation Learning+3GEFL: Extended Filtration Learning for Graph Classification
Extended persistence is a technique from topological data analysis to obtain global multiscale topological information from a graph. This includes information about connected components and cycles that are captured by th…
ClassificationGraph ClassificationGraph Representation LearningRepresentation Learning+1Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from financial fraud detection to fake news detection. However, current GAD methods largely overlook the fairness problem, which migh…
Anomaly DetectionDecision MakingDisentanglementFairness+5Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
Binary code similarity detection is an important problem with applications in areas such as malware analysis, vulnerability research and license violation detection. This paper proposes a novel graph neural network archi…
Graph Neural NetworkGraph Representation LearningMalware AnalysisMetric Learning+1Graph External Attention Enhanced Transformer
The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attenti…
Graph Representation LearningRepresentation LearningAugmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification
Major depressive disorder (MDD) is a common mental disorder that typically affects a person's mood, cognition, behavior, and physical health. Resting-state functional magnetic resonance imaging (rs-fMRI) data are widely …
Data AugmentationDiagnosticDomain AdaptationFunctional Connectivity+2Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
Anomaly detection in continuous-time dynamic graphs is an emerging field yet under-explored in the context of learning algorithms. In this paper, we pioneer structured analyses of link-level anomalies and graph represent…
Anomaly DetectionGraph LearningGraph Representation LearningLink Prediction+1Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
The dynamics of information diffusion within graphs is a critical open issue that heavily influences graph representation learning, especially when considering long-range propagation. This calls for principled approaches…
Graph Representation LearningRepresentation LearningGCondenser: Benchmarking Graph Condensation
Large-scale graphs are valuable for graph representation learning, yet the abundant data in these graphs hinders the efficiency of the training process. Graph condensation (GC) alleviates this issue by compressing the la…
BenchmarkingGraph Representation LearningRepresentation LearningHC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph da…
DecoderGraph ClassificationGraph Representation LearningNode Classification+1