Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
An Efficient Hybridization of Graph Representation Learning and Metaheuristics for the Constrained Incremental Graph Drawing Problem
Hybridizing machine learning techniques with metaheuristics has attracted significant attention in recent years. Many attempts employ supervised or reinforcement learning to support the decision-making of heuristic metho…
Graph Representation LearningReinforcement LearningGraph LearningSVDformer: Direction-Aware Spectral Graph Embedding Learning via SVD and Transformer
Directed graphs are widely used to model asymmetric relationships in real-world systems. However, existing directed graph neural networks often struggle to jointly capture directional semantics and global structural patt…
Graph Representation LearningNode ClassificationGraph EmbeddingTowards the Next-generation Bayesian Network Classifiers
Bayesian network classifiers provide a feasible solution to tabular data classification, with a number of merits like high time and memory efficiency, and great explainability. However, due to the parameter explosion and…
Graph Representation LearningGRAVITY: A Controversial Graph Representation Learning for Vertex Classification
In the quest of accurate vertex classification, we introduce GRAVITY (Graph-based Representation leArning via Vertices Interaction TopologY), a framework inspired by physical systems where objects self-organize under att…
Graph Representation LearningDisentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation
Next Point-of-Interest (POI) recommendation is a research hotspot in business intelligence, where users' spatial-temporal transitions and social relationships play key roles. However, most existing works model spatial an…
Graph Representation LearningGraph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structured data. However, a fundamental challenge, i.e., codebook collapse, remains und…
Graph Representation LearningTANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows
We introduce TANGO -- a dynamical systems inspired framework for graph representation learning that governs node feature evolution through a learned energy landscape and its associated descent dynamics. At the core of ou…
Graph Representation LearningGraph Neural NetworkGraph ClassificationGraph LearningDeformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysis
Accurate classification of Whole Slide Images (WSIs) and Regions of Interest (ROIs) is a fundamental challenge in computational pathology. While mainstream approaches often adopt Multiple Instance Learning (MIL), they st…
Graph Representation LearningMultiple Instance LearningGraph Representation Learning with Massive Unlabeled Data for Rumor Detection
With the development of social media, rumors spread quickly, cause great harm to society and economy. Thereby, many effective rumor detection methods have been developed, among which the rumor propagation structure learn…
Graph Representation LearningSelf-Supervised LearningGraph Embedding in the Graph Fractional Fourier Transform Domain
Spectral graph embedding plays a critical role in graph representation learning by generating low-dimensional vector representations from graph spectral information. However, the embedding space of traditional spectral e…
Graph Representation LearningGraph EmbeddingNeighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks
Graph convolutional networks (GCNs) are a powerful tool for graph representation learning. Due to the recursive neighborhood aggregations employed by GCNs, efficient training methods suffer from a lack of theoretical gua…
Graph Representation LearningNode ClassificationExploring Adaptive Structure Learning for Heterophilic Graphs
Graph Convolutional Networks (GCNs) gained traction for graph representation learning, with recent attention on improving performance on heterophilic graphs for various real-world applications. The localized feature aggr…
Graph Representation LearningNode ClassificationGLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs, where connected nodes differ in features or class labels. This limitat…
Graph Representation LearningThe Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs
Pattern recognition with concise and flat AND-rules makes the Tsetlin Machine (TM) both interpretable and efficient, while the power of Tsetlin automata enables accuracy comparable to deep learning on an increasing numbe…
Graph Representation LearningReinforcement LearningRecommendation SystemsImage ClassificationResearch on the application of graph data structure and graph neural network in node classification/clustering tasks
Graph-structured data are pervasive across domains including social networks, biological networks, and knowledge graphs. Due to their non-Euclidean nature, such data pose significant challenges to conventional machine le…
Graph Representation LearningGraph Neural NetworkNode ClassificationKnowledge GraphsSMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs
Knowledge graph representation learning approaches provide a mapping between symbolic knowledge in the form of triples in a knowledge graph (KG) and their feature vectors. Knowledge graph embedding (KGE) models often rep…
Graph EmbeddingGraph Representation LearningKnowledge Graph EmbeddingKnowledge Graphs+1Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning
Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improved neighborhood knowledge capture. Howeve…
Graph Representation LearningFraud DetectionStr-GCL: Structural Commonsense Driven Graph Contrastive Learning
Graph Contrastive Learning (GCL) is a widely adopted approach in self-supervised graph representation learning, applying contrastive objectives to produce effective representations. However, current GCL methods primarily…
Graph Representation LearningContrastive LearningTransferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits
Graph representation learning on Analog-Mixed Signal (AMS) circuits is crucial for various downstream tasks, e.g., parasitic estimation. However, the scarcity of design data, the unbalanced distribution of labels, and th…
Graph Representation LearningContrastive LearningNode ClassificationFew-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction
Graph representation learning is a powerful method to extract features from graph-structured data, such as analog/mixed-signal (AMS) circuits. However, training deep learning models for AMS designs is severely limited by…
Graph Representation LearningZero-Shot LearningFew-Shot LearningLink Prediction