Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Distribution Preserving Graph Representation Learning
Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly-expressive GNN…
Graph ClassificationGraph LearningGraph Neural NetworkGraph Representation Learning+1Automated Data Augmentations for Graph Classification
Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations that preserve labels. This is relatively st…
ClassificationData AugmentationGraph ClassificationBayesian Deep Learning for Graphs
The adaptive processing of structured data is a long-standing research topic in machine learning that investigates how to automatically learn a mapping from a structured input to outputs of various nature. Recently, ther…
Deep LearningGraph ClassificationGraph LearningMalware ClassificationRelation Regularized Scene Graph Generation
Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects ar…
Graph ClassificationGraph GenerationObjectPredicate Classification+4Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
Differentially private GNNs (Graph Neural Networks) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy. In particular, a recent study proposes an alg…
Data PoisoningGraph ClassificationModel-Agnostic Augmentation for Accurate Graph Classification
Given a graph dataset, how can we augment it for accurate graph classification? Graph augmentation is an essential strategy to improve the performance of graph-based tasks, and has been widely utilized for analyzing web …
ClassificationGraph ClassificationmodelGraph Masked Autoencoders with Transformers
Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenarios due to the fact that deep transformer…
DecoderGraph ClassificationNode ClassificationGRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs
Most methods for explaining black-box classifiers (e.g. on tabular data, images, or time series) rely on measuring the impact that removing/perturbing features has on the model output. This forces the explanation languag…
ClassificationComputational EfficiencyGraph ClassificationTime Series AnalysisG-Mixup: Graph Data Augmentation for Graph Classification
This work develops \emph{mixup for graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels between two random samples. Traditionally…
ClassificationData AugmentationGraph ClassificationFairness Amidst Non-IID Graph Data: A Literature Review
The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data is independent and ident…
FairnessGraph ClassificationGraph GenerationGraph Neural Network with Curriculum Learning for Imbalanced Node Classification
Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification. In this work, we reveal the vulnerability of GNN to the imbalance of node labels. Traditional solutions for …
ClassificationGraph ClassificationGraph Neural Networkimbalanced classification+2Differentially Private Graph Classification with GNNs
Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications such as the analysis of social networks, protein interactions and molecules. Several among th…
BIG-bench Machine LearningClassificationGraph ClassificationGraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network
With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on c…
C++ codeGraph AttentionGraph ClassificationGeneralization Analysis of Message Passing Neural Networks on Large Random Graphs
Message passing neural networks (MPNN) have seen a steep rise in popularity since their introduction as generalizations of convolutional neural networks to graph-structured data, and are now considered state-of-the-art t…
Graph ClassificationInvestigating Transfer Learning in Graph Neural Networks
Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems: resulting in …
ClassificationGraph ClassificationNode ClassificationTransfer LearningGRPE: Relative Positional Encoding for Graph Transformer
We propose a novel positional encoding for learning graph on Transformer architecture. Existing approaches either linearize a graph to encode absolute position in the sequence of nodes, or encode relative position with a…
Graph ClassificationGraph RegressionGraph Representation LearningMolecular Property Prediction+2Discovering Invariant Rationales for Graph Neural Networks
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction. Unfortunately, the leading rationalization models ofte…
Graph ClassificationImage Classification using Graph Neural Network and Multiscale Wavelet Superpixels
Prior studies using graph neural networks (GNNs) for image classification have focused on graphs generated from a regular grid of pixels or similar-sized superpixels. In the latter, a single target number of superpixels …
ClassificationGraph ClassificationGraph Neural Networkimage-classification+2Revisiting Global Pooling through the Lens of Optimal Transport
Global pooling is one of the most significant operations in many machine learning models and tasks, whose implementation, however, is often empirical in practice. In this study, we develop a novel and solid global poolin…
Graph ClassificationGraph Embeddingimage-classificationImage ClassificationRepresenting Long-Range Context for Graph Neural Networks with Global Attention
Graph neural networks are powerful architectures for structured datasets. However, current methods struggle to represent long-range dependencies. Scaling the depth or width of GNNs is insufficient to broaden receptive fi…
Graph ClassificationGraph Embedding