Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities
Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously e…
Document RankingGraph LearningGraph Representation LearningInformation Retrieval+6Multi-View Node Pruning for Accurate Graph Representation
Graph pooling, which compresses a whole graph into a smaller coarsened graph, is an essential component of graph representation learning. To efficiently compress a given graph, graph pooling methods often drop their node…
Graph Representation LearningRepresentation LearningTowards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
Long-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks, offering limited insight into long-range interactions. Curre…
Graph Representation LearningNode ClassificationRepresentation LearningTransductive LearningFine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis
Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, and most public datasets contain no clinic…
DiagnosticGraph Neural NetworkGraph Representation LearningMedical Image Analysis+1Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space
The Influence Maximization (IM) problem aims to find a small set of influential users to maximize their influence spread in a social network. Traditional methods rely on fixed diffusion models with known parameters, limi…
Graph Representation LearningRepresentation LearningDemocratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs
Data augmentation is necessary for graph representation learning due to the scarcity and noise present in graph data. Most of the existing augmentation methods overlook the context information inherited from the dataset …
Data AugmentationGraph LearningGraph Representation LearningKnowledge Graphs+4Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin
Graph neural networks are gaining attention in fifth-generation (5G) core network digital twins, which are data-driven complex systems with numerous components. Analyzing these data can be challenging due to rare failure…
Graph ClassificationGraph Neural NetworkGraph Representation Learningimbalanced classification+1DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining
Pretraining models with unsupervised graph representation learning has led to significant advancements in domains such as social network analysis, molecular design, and electronic design automation (EDA). However, prior …
Contrastive LearningData AugmentationGraph Neural NetworkGraph Representation Learning+2Graph Contrastive Learning for Connectome Classification
With recent advancements in non-invasive techniques for measuring brain activity, such as magnetic resonance imaging (MRI), the study of structural and functional brain networks through graph signal processing (GSP) has …
ClassificationContrastive LearningData AugmentationFunctional Connectivity+4Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics
Recent technological advances have introduced new high-throughput methods for studying host-virus interactions, but testing synergistic interactions between host gene pairs during infection remains relatively slow and la…
Active LearningExperimental DesignGraph Representation LearningRepresentation Learning+1Learning Efficient Positional Encodings with Graph Neural Networks
Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity …
Graph RegressionGraph Representation LearningPositionRepresentation LearningLeveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning
Graph representation learning has emerged as a cornerstone for tasks like node classification and link prediction, yet prevailing self-supervised learning (SSL) methods face challenges such as computational inefficiency,…
Bayesian InferenceGraph Representation LearningLink PredictionNode Classification+3Spectro-Riemannian Graph Neural Networks
Can integrating spectral and curvature signals unlock new potential in graph representation learning? Non-Euclidean geometries, particularly Riemannian manifolds such as hyperbolic (negative curvature) and spherical (pos…
Graph Representation LearningLink PredictionNode ClassificationRepresentation LearningContrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to Global
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in various graph representation learning tasks. However, most existing GNNs focus primarily on capturing local information through explicit graph co…
Contrastive LearningGraph Representation LearningNode ClassificationPseudo Label+1Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which causes all node representations to conv…
Graph Neural NetworkGraph Representation LearningMambaNode Classification+1Deep Modularity Networks with Diversity--Preserving Regularization
Graph clustering plays a crucial role in graph representation learning but often faces challenges in achieving feature-space diversity. While Deep Modularity Networks (DMoN) leverage modularity maximization and collapse …
ClusteringDiversityGraph ClusteringGraph Representation Learning+1Graph Representation Learning with Diffusion Generative Models
Diffusion models have established themselves as state-of-the-art generative models across various data modalities, including images and videos, due to their ability to accurately approximate complex data distributions. U…
DenoisingGraph Representation LearningRepresentation LearningCommunity-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs
Dynamic graph representation learning plays a crucial role in understanding evolving behaviors. However, existing methods often struggle with flexibility, adaptability, and the preservation of temporal and structural dyn…
Graph Representation LearningLink PredictionRepresentation LearningOptimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks
Blockchain technology, with implications in the financial domain, offers data in the form of large-scale transaction networks. Analyzing transaction networks facilitates fraud detection, market analysis, and supports gov…
Fraud DetectionGraph Representation LearningIncremental LearningNode Classification+1Enhancing Graph Representation Learning with Localized Topological Features
Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for graph representation learning, are limited in their representation power. …
Graph LearningGraph Representation LearningLink PredictionNode Classification+1