Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing
Graph classification is a fundamental task in domains ranging from molecular property prediction to materials design. While graph neural networks (GNNs) achieve strong performance by learning expressive representations v…
Molecular Property PredictionGraph ClassificationGraph LearningCross-View Topology-Aware Graph Representation Learning
Graph classification has gained significant attention due to its applications in chemistry, social networks, and bioinformatics. While Graph Neural Networks (GNNs) effectively capture local structural patterns, they ofte…
Graph Representation LearningGraph ClassificationContrastive LearningSA$^{2}$GFM: Enhancing Robust Graph Foundation Models with Structure-Aware Semantic Augmentation
We present Graph Foundation Models (GFMs) which have made significant progress in various tasks, but their robustness against domain noise, structural perturbations, and adversarial attacks remains underexplored. A key l…
Graph ClassificationDomain AdaptationSCLARO: A Dataset for Grounded Scenario-Level Scene Understanding and ScenarioCLIP for Benchmarking
In the paradigm of computer vision-based precise real-world scene understanding, joint reasoning in terms of contextual understanding about the objects present in a scene, their inter-object relations, and the action bei…
Knowledge DistillationGraph ClassificationScene UnderstandingObject DetectionSelf-Adaptive Graph Mixture of Models
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over graph-structured data, yet recent studies have shown that their performance gains are beginning to plateau. In many cases, well-established mo…
Graph ClassificationNode ClassificationLink PredictionGraph LearningOn topological descriptors for graph products
Topological descriptors have been increasingly utilized for capturing multiscale structural information in relational data. In this work, we consider various filtrations on the (box) product of graphs and the effect on t…
Graph ClassificationStuart-Landau Oscillatory Graph Neural Network
Oscillatory Graph Neural Networks (OGNNs) are an emerging class of physics-inspired architectures designed to mitigate oversmoothing and vanishing gradient problems in deep GNNs. In this work, we introduce the Complex-Va…
Graph ClassificationGraph Neural NetworkNode ClassificationGraph RegressionGRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph tasks and domains. However, existing GF…
Graph ClassificationGraph SimilarityRobust GNN Watermarking via Implicit Perception of Topological Invariants
Graph Neural Networks (GNNs) are valuable intellectual property, yet many watermarks rely on backdoor triggers that break under common model edits and create ownership ambiguity. We present InvGNN-WM, which ties ownershi…
Knowledge DistillationGraph ClassificationDynBERG: Dynamic BERT-based Graph neural network for financial fraud detection
Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widel…
Graph Neural NetworkGraph ClassificationFraud DetectionEnhancing Graph Classification Robustness with Singular Pooling
Graph Neural Networks (GNNs) have achieved strong performance across a range of graph representation learning tasks, yet their adversarial robustness in graph classification remains underexplored compared to node classif…
Graph Representation LearningAdversarial RobustnessGraph ClassificationNode ClassificationPruning and Quantization Impact on Graph Neural Networks
Graph neural networks (GNNs) are known to operate with high accuracy on learning from graph-structured data, but they suffer from high computational and resource costs. Neural network compression methods are used to redu…
Neural Network CompressionGraph ClassificationNode ClassificationLink PredictionEnhancing Graph Neural Networks: A Mutual Learning Approach
Knowledge distillation (KD) techniques have emerged as a powerful tool for transferring expertise from complex teacher models to lightweight student models, particularly beneficial for deploying high-performance models i…
Knowledge DistillationGraph ClassificationUniGTE: Unified Graph-Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains
Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large language models (LLMs) struggle to capture gr…
Zero-shot GeneralizationGraph ClassificationNode ClassificationGraph RegressionRedundancy-Aware Test-Time Graph Out-of-Distribution Detection
Distributional discrepancy between training and test data can lead models to make inaccurate predictions when encountering out-of-distribution (OOD) samples in real-world applications. Although existing graph OOD detecti…
Out-of-Distribution DetectionGraph ClassificationT3former: Temporal Graph Classification with Topological Machine Learning
Temporal graph classification plays a critical role in applications such as cybersecurity, brain connectivity analysis, social dynamics, and traffic monitoring. Despite its significance, this problem remains underexplore…
Graph ClassificationLink PredictionGraph LearningTransfer Learning on Edge Connecting Probability Estimation under Graphon Model
Graphon models provide a flexible nonparametric framework for estimating latent connectivity probabilities in networks, enabling a range of downstream applications such as link prediction and data augmentation. However, …
Graph ClassificationTransfer LearningData AugmentationLink PredictionGDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification
We present GDLNN, a new graph machine learning architecture, for graph classification tasks. GDLNN combines a domain-specific programming language, called GDL, with neural networks. The main strength of GDLNN lies in its…
Graph ClassificationGraph LearningStealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph classification, but remain vulnerable to backdoor attacks that implant imperceptible …
Graph ClassificationNode ClassificationAnomaly DetectionLink PredictionMUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models
Interpretability is essential for deploying deep learning models in symbolic music analysis, yet most research emphasizes model performance over explanation. To address this, we introduce MUSE-Explainer, a new method tha…
Graph ClassificationGraph Neural Network