Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Self-supervision meets kernel graph neural models: From architecture to augmentations
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the most prevailing algorithmic tool. Despi…
Data AugmentationGraph ClassificationGraph Representation LearningRepresentation Learning+1SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
The emerging graph Transformers have achieved impressive performance for graph representation learning over graph neural networks (GNNs). In this work, we regard the self-attention mechanism, the core module of graph Tra…
Graph Representation LearningNode ClassificationRepresentation LearningSelf-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks
Graphs have become an important modeling tool for web applications, and Graph Neural Networks (GNNs) have achieved great success in graph representation learning. However, the performance of traditional GNNs heavily reli…
Contrastive LearningGraph Representation LearningPrompt LearningRepresentation LearningMAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning
Advance Persistent Threats (APTs), adopted by most delicate attackers, are becoming increasing common and pose great threat to various enterprises and institutions. Data provenance analysis on provenance graphs has emerg…
Graph Representation LearningOutlier DetectionRepresentation LearningSGA: A Graph Augmentation Method for Signed Graph Neural Networks
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links. However, three key challenges hinder current SGNN-based signed graph repre…
Data AugmentationGraph Representation LearningLink Sign PredictionRepresentation LearningTopology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation
Hypergraphs, with their capacity to depict high-order relationships, have emerged as a significant extension of traditional graphs. Although Graph Neural Networks (GNNs) have remarkable performance in graph representatio…
Graph Representation LearningNode ClassificationRepresentation LearningDoes Graph Distillation See Like Vision Dataset Counterpart?
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condensation methods primarily focus on optimiz…
Anomaly DetectionDataset DistillationGraph Representation LearningLink Prediction+1An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems
In recent years, there has been a notable surge in research on machine learning techniques for combinatorial optimization. It has been shown that learning-based methods outperform traditional heuristics and mathematical …
Combinatorial OptimizationComputational EfficiencyGraph LearningGraph Representation Learning+3Audio Event-Relational Graph Representation Learning for Acoustic Scene Classification
Most deep learning-based acoustic scene classification (ASC) approaches identify scenes based on acoustic features converted from audio clips containing mixed information entangled by polyphonic audio events (AEs). Howev…
Acoustic Scene ClassificationGraph Representation LearningRepresentation LearningScene ClassificationCertifiably Robust Graph Contrastive Learning
Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attacks on both the graph structure and node a…
Contrastive LearningGraph Representation LearningRepresentation LearningDINE: Dimensional Interpretability of Node Embeddings
Graphs are ubiquitous due to their flexibility in representing social and technological systems as networks of interacting elements. Graph representation learning methods, such as node embeddings, are powerful approaches…
Graph Representation LearningLink PredictionRepresentation LearningA Unified View on Neural Message Passing with Opinion Dynamics for Social Networks
Social networks represent a common form of interconnected data frequently depicted as graphs within the domain of deep learning-based inference. These communities inherently form dynamic systems, achieving stability thro…
Graph Representation LearningRepresentation LearningSociologyTransformers are efficient hierarchical chemical graph learners
Transformers, adapted from natural language processing, are emerging as a leading approach for graph representation learning. Contemporary graph transformers often treat nodes or edges as separate tokens. This approach l…
Graph Representation LearningRepresentation LearningLearning node representation via Motif Coarsening
Motifs, as fundamental units of the graph, play a significant role in modeling complex systems in a variety of domains, including social networks, as well as biology and neuroscience. Motif preservation is a widely studi…
Graph Representation LearningRepresentation LearningAugment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings
Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks. However, the typical lack of interpretability…
Data AugmentationGraph Representation LearningRepresentation LearningGraph Representation Learning Towards Patents Network Analysis
Patent analysis has recently been recognized as a powerful technique for large companies worldwide to lend them insight into the age of competition among various industries. This technique is considered a shortcut for de…
Entity ResolutionGraph Representation LearningRepresentation LearningGraph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks
Graph Neural Networks (GNNs) have become popular in Graph Representation Learning (GRL). One fundamental application is few-shot node classification. Most existing methods follow the meta learning paradigm, showing the a…
CoLAContrastive LearningGraph Representation LearningMeta-Learning+2Deep Prompt Tuning for Graph Transformers
Graph transformers have gained popularity in various graph-based tasks by addressing challenges faced by traditional Graph Neural Networks. However, the quadratic complexity of self-attention operations and the extensive…
Graph Representation LearningRepresentation LearningUniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs
Irregular data in real-world are usually organized as heterogeneous graphs (HGs) consisting of multiple types of nodes and edges. To explore useful knowledge from real-world data, both the large-scale encyclopedic HG dat…
AttributeGraph LearningGraph Representation LearningKnowledge Graphs+2Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning
Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space. While there exist graph neural networks that leverage hyperbolic or spherical spaces to learn repres…
Graph ReconstructionGraph Representation LearningNode ClassificationRepresentation Learning