Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks. In DropGNNs, we execute multiple runs of a GNN on the input graph, with some o…
Graph ClassificationGraph RegressionMolecular Property PredictionGraph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation
Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult. To tackle this challenge, we present the first Mixup-li…
Data AugmentationGraph ClassificationInferential SIR-GN: Scalable Graph Representation Learning
Graph representation learning methods generate numerical vector representations for the nodes in a network, thereby enabling their use in standard machine learning models. These methods aim to preserve relational informa…
Graph ClassificationGraph Representation LearningRepresentation LearningAugmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices
Unsupervised graph representation learning is critical to a wide range of applications where labels may be scarce or expensive to procure. Contrastive learning (CL) is an increasingly popular paradigm for such settings a…
ClassificationContrastive LearningData AugmentationDocument Classification+5Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
Theoretical analyses for graph learning methods often assume a complete observation of the input graph. Such an assumption might not be useful for handling any-size graphs due to the scalability issues in practice. In th…
ClassificationGeneralization BoundsGraph ClassificationGraph LearningAdversarial Attacks on Graph Classification via Bayesian Optimisation
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vuln…
Adversarial RobustnessBayesian OptimisationClassificationGraph ClassificationMulti network InfoMax: A pre-training method involving graph convolutional networks
Discovering distinct features and their relations from data can help us uncover valuable knowledge crucial for various tasks, e.g., classification. In neuroimaging, these features could help to understand, classify, and …
Graph ClassificationTopological Relational Learning on Graphs
Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are vulnerable to graph perturbations. To a…
Graph ClassificationNode ClassificationRelational ReasoningRepresentation LearningInfoGCL: Information-Aware Graph Contrastive Learning
Various graph contrastive learning models have been proposed to improve the performance of learning tasks on graph datasets in recent years. While effective and prevalent, these models are usually carefully customized. I…
Contrastive LearningGraph ClassificationGraph LearningGraph Representation Learning+2Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning on graph data still remains a challenge f…
Binary ClassificationGraph ClassificationGraph Neural NetworkGraph Representation Learning+2Nested Graph Neural Networks
Graph neural network (GNN)'s success in graph classification is closely related to the Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features to a center node, both 1-WL and GNN obtain a…
Graph ClassificationGraph Neural NetworkGraph Property PredictionWatermarking Graph Neural Networks based on Backdoor Attacks
Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. Building a powerful GNN model is not a trivial task, as it requires a large amount of training data, powerful computing…
ClassificationGraph ClassificationModel extractionNode Classification+1Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification…
ClassificationGraph ClassificationLink PredictionNode ClassificationifMixup: Interpolating Graph Pair to Regularize Graph Classification
We present a simple and yet effective interpolation-based regularization technique, aiming to improve the generalization of Graph Neural Networks (GNNs) on supervised graph classification. We leverage Mixup, an effective…
ClassificationGraph ClassificationGraph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos
Temporal activity localization in long videos is an important problem. The cost of obtaining frame level label for long Wireless Capsule Endoscopy (WCE) videos is prohibitive. In this paper, we propose an end-to-end temp…
Change Point DetectionGraph ClassificationSpecificityCapsule Graph Neural Networks with EM Routing
To effectively classify graph instances, graph neural networks need to have the capability to capture the part-whole relationship existing in a graph. A capsule is a group of neurons representing complicated properties o…
Graph ClassificationAdapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean data, such as chemical networks, brain networks, and social networks, modelling complex relationships and interdependency between objects. Recently…
Graph ClassificationInference AttackMembership Inference AttackSoGCN: Second-Order Graph Convolutional Networks
Graph Convolutional Networks (GCN) with multi-hop aggregation is more expressive than one-hop GCN but suffers from higher model complexity. Finding the shortest aggregation range that achieves comparable expressiveness a…
Graph ClassificationGraph RegressionNode ClassificationOpen-Ended Question AnsweringWell-classified Examples are Underestimated in Classification with Deep Neural Networks
The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary. For instance, when training with cros…
ClassificationGraph Classificationimage-classificationImage Classification+5Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification
Graph convolutional network (GCN) is one of the most favorable semi-supervised approaches, which demonstrates encouraging performance for hyperspectral image classification (HSIC), especially under the condition of small…
ClassificationComputational EfficiencyGraph ClassificationHyperspectral Image Classification+1