Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown…
Adversarial RobustnessContrastive LearningGraph ClassificationGraph Representation Learning+2Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities. Anticipated to signif…
Graph ClassificationEMP: Effective Multidimensional Persistence for Graph Representation Learning
Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine learning tasks that spans from manifold learning to graph classification. A pivotal technique within TDA is persistent homology (PH)…
Graph ClassificationGraph Representation LearningRepresentation LearningTopological Data AnalysisTensor-view Topological Graph Neural Network
Graph classification is an important learning task for graph-structured data. Graph neural networks (GNNs) have recently gained growing attention in graph learning and have shown significant improvements in many importan…
Graph ClassificationGraph LearningGraph Neural NetworkRepresentation LearningThrough the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
Graph Neural Networks have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of augmentation methods, issues like graph p…
Data AugmentationGraph ClassificationGNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper, we propose a new graph neural network architecture that substitutes classical message passing with …
Graph ClassificationGraph Neural NetworkContrastive Learning with Negative Sampling Correction
As one of the most effective self-supervised representation learning methods, contrastive learning (CL) relies on multiple negative pairs to contrast against each positive pair. In the standard practice of contrastive le…
Contrastive LearningData AugmentationGraph ClassificationRepresentation LearningOn the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social Networks
This paper studies four Graph Neural Network architectures (GNNs) for a graph classification task on a synthetic dataset created using classic generative models of Network Science. Since the synthetic networks do not con…
Graph ClassificationGraph Neural NetworkAccurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while…
Graph ClassificationGraph Representation LearningOut-of-Distribution DetectionRepresentation LearningEffective backdoor attack on graph neural networks in link prediction tasks
Graph Neural Networks (GNNs) are a class of deep learning models capable of processing graph-structured data, and they have demonstrated significant performance in a variety of real-world applications. Recent studies hav…
Backdoor AttackGraph ClassificationLink PredictionNode ClassificationView-based Explanations for Graph Neural Networks
Generating explanations for graph neural networks (GNNs) has been studied to understand their behavior in analytical tasks such as graph classification. Existing approaches aim to understand the overall results of GNNs r…
Graph ClassificationOn the Expressive Power of Graph Neural Networks
The study of Graph Neural Networks has received considerable interest in the past few years. By extending deep learning to graph-structured data, GNNs can solve a diverse set of tasks in fields including social science, …
Graph ClassificationSaliency-Aware Regularized Graph Neural Network
The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, …
ClassificationGraph ClassificationGraph Neural NetworkRepresentation Learning+2On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
Neural networks are increasingly finding their way into the realm of graphs and modeling relationships between features. Concurrently graph neural network explanation approaches are being invented to uncover relationship…
Graph ClassificationGraph Neural NetworkFine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
Recently, the paradigm of pre-training and fine-tuning graph neural networks has been intensively studied and applied in a wide range of graph mining tasks. Its success is generally attributed to the structural consisten…
Graph ClassificationGraph LearningGraph MiningGraph Neural Network+1Domain Adaptive Graph Classification
Despite the remarkable accomplishments of graph neural networks (GNNs), they typically rely on task-specific labels, posing potential challenges in terms of their acquisition. Existing work have been made to address this…
ClassificationDomain AdaptationGraph ClassificationGraph Representation Learning+2Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity
Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. However, they mainly focus on node classificatio…
Active LearningDiversityGraph ClassificationNode ClassificationLightGCN: Evaluated and Enhanced
This paper analyses LightGCN in the context of graph recommendation algorithms. Despite the initial design of Graph Convolutional Networks for graph classification, the non-linear operations are not always essential. Lig…
Graph ClassificationGraph Representation LearningRecommendation SystemsImproving Subgraph-GNNs via Edge-Level Ego-Network Encodings
We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats…
Graph ClassificationGraph RegressionIsomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning
Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice, it is desirable to develop t…
Graph ClassificationGraph LearningGraph Neural NetworkGraph Representation Learning+3