Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements
Graphs are essential data structures for modeling complex interactions in domains such as social networks, molecular structures, and biological systems. Graph-level tasks, which predict properties or classes for the enti…
Contrastive LearningGraph ClassificationMolecular Property PredictionProperty Prediction+1Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning
While message passing graph neural networks result in informative node embeddings, they may suffer from describing the topological properties of graphs. To this end, node filtration has been widely used as an attempt to …
Graph ClassificationGraph Representation LearningRepresentation LearningGraph Size-imbalanced Learning with Energy-guided Structural Smoothing
Graph is a prevalent data structure employed to represent the relationships between entities, frequently serving as a tool to depict and simulate numerous systems, such as molecules and social networks. However, real-wor…
Graph ClassificationMulti-view Fuzzy Graph Attention Networks for Enhanced Graph Learning
Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, existing models struggle to effectively captu…
Graph AttentionGraph ClassificationGraph LearningCluster-guided Contrastive Class-imbalanced Graph Classification
This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) hav…
ClassificationContrastive LearningGraph ClassificationSemi-Implicit Neural Ordinary Differential Equations
Classical neural ODEs trained with explicit methods are intrinsically limited by stability, crippling their efficiency and robustness for stiff learning problems that are common in graph learning and scientific machine l…
Graph ClassificationGraph LearningRobustness of Graph Classification: failure modes, causes, and noise-resistant loss in Graph Neural Networks
Graph Neural Networks (GNNs) are powerful at solving graph classification tasks, yet applied problems often contain noisy labels. In this work, we study GNN robustness to label noise, demonstrate GNN failure modes when m…
Graph ClassificationInductive BiasTraining MLPs on Graphs without Supervision
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive app…
Fraud DetectionGraph ClassificationGraph LearningInductive Bias+3GQWformer: A Quantum-based Transformer for Graph Representation Learning
Graph Transformers (GTs) have demonstrated significant advantages in graph representation learning through their global attention mechanisms. However, the self-attention mechanism in GTs tends to neglect the inductive bi…
Graph ClassificationGraph Representation LearningInductive BiasRepresentation LearningGradient Inversion Attack on Graph Neural Networks
Graph federated learning is of essential importance for training over large graph datasets while protecting data privacy, where each client stores a subset of local graph data, while the server collects the local gradien…
Federated LearningGraph ClassificationNode ClassificationGrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
Graph Transformers (GTs) have demonstrated remarkable performance in incorporating various graph structure information, e.g., long-range structural dependency, into graph representation learning. However, self-attention …
Graph ClassificationGraph Representation LearningNode ClassificationRepresentation LearningFrom Graph Diffusion to Graph Classification
Generative models such as diffusion models have achieved remarkable success in state-of-the-art image and text tasks. Recently, score-based diffusion models have extended their success beyond image generation, showing co…
ClassificationGraph ClassificationImage GenerationMADE: Graph Backdoor Defense with Masked Unlearning
Graph Neural Networks (GNNs) have garnered significant attention from researchers due to their outstanding performance in handling graph-related tasks, such as social network analysis, protein design, and so on. Despite …
backdoor defenseDrug DiscoveryGraph ClassificationProtein DesignConditional Distribution Learning on Graphs
Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally, successive layers in graph neural networ…
Contrastive LearningData AugmentationGraph ClassificationGraph Neural Network+2The GECo algorithm for Graph Neural Networks Explanation
Graph Neural Networks (GNNs) are powerful models that can manage complex data sources and their interconnection links. One of GNNs' main drawbacks is their lack of interpretability, which limits their application in sens…
Graph ClassificationEfficient and Robust Continual Graph Learning for Graph Classification in Biology
Graph classification is essential for understanding complex biological systems, where molecular structures and interactions are naturally represented as graphs. Traditional graph neural networks (GNNs) perform well on st…
ClassificationGraph ClassificationGraph LearningGraph Neural Network Generalization with Gaussian Mixture Model Based Augmentation
Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly to unseen or out-of-distribution (OOD) data. These challenges are exa…
Data AugmentationDiversityGraph ClassificationGraph Neural NetworkBoolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network
Boolean circuit is a computational graph that consists of the dynamic directed graph structure and static functionality. The commonly used logic optimization and Boolean matching-based transformation can change the behav…
ClassificationGraph ClassificationGraph Neural NetworkMBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings
In many industrial applications, it is common that the graph embeddings generated from training GNNs are used in an ensemble model where the embeddings are combined with other tabular features (e.g., original node or edg…
Graph ClassificationMulti-Armed BanditsRAGraph: A General Retrieval-Augmented Graph Learning Framework
Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In thi…
Graph ClassificationGraph LearningLink PredictionNode Classification+1