Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
GraphAttacker: A General Multi-Task GraphAttack Framework
Graph neural networks (GNNs) have been successfully exploited in graph analysis tasks in many real-world applications. The competition between attack and defense methods also enhances the robustness of GNNs. In this comp…
General ClassificationGenerative Adversarial NetworkGraph ClassificationLink Prediction+1Membership Inference Attack on Graph Neural Networks
Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focus on how trained GNN models could leak i…
Graph ClassificationInference AttackLink PredictionMembership Inference Attack+1Label Contrastive Coding based Graph Neural Network for Graph Classification
Graph classification is a critical research problem in many applications from different domains. In order to learn a graph classification model, the most widely used supervision component is an output layer together with…
ClassificationContrastive LearningGeneral ClassificationGraph Classification+2Optimisation of Spectral Wavelets for Persistence-based Graph Classification
A graph's spectral wavelet signature determines a filtration, and consequently an associated set of extended persistence diagrams. We propose a framework that optimises the choice of wavelet for a dataset of graphs, such…
ClassificationGeneral ClassificationGraph ClassificationThe Shapley Value of Classifiers in Ensemble Games
What is the value of an individual model in an ensemble of binary classifiers? We answer this question by introducing a class of transferable utility cooperative games called \textit{ensemble games}. In machine learning …
ClassificationEnsemble PruningGraph ClassificationMulti-level Graph Matching Networks for Deep and Robust Graph Similarity Learning
While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to graph similarity learning. Recent works have considered e…
Graph ClassificationGraph MatchingGraph Neural NetworkGraph SimilarityPolynomial Graph Convolutional Networks
Graph Convolutional Neural Networks (GCNs) exploit convolution operators, based on some neighborhood aggregating scheme, to compute representations of graphs. The most common convolution operators only exploit local topo…
Graph ClassificationGraph Neural NetworkGraph-Graph Similarity Network
Graph classification aims to predict the class label for an entire graph. Recently, Graph Neural Networks (GNNs)-based approaches become an essential strand to learn low-dimensional continuous embeddings of the entire gr…
ClassificationGeneral ClassificationGraph ClassificationGraph Similarity+1On Single-environment Extrapolations in Graph Classification and Regression Tasks
Extrapolation in graph classification/regression remains an underexplored area of an otherwise rapidly developing field. Our work contributes to a growing literature by providing the first systematic counterfactual model…
ClassificationcounterfactualGeneral ClassificationGraph Classification+1One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting
Spatiotemporal forecasting plays an essential role in intelligent transportation systems (ITS) and numerous applications, such as route planning, navigation, and automatic driving. Deep Spatiotemporal Graph Neural Networ…
Graph ClassificationLearning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling
Neural networks have become the standard for image classification tasks. On one hand, convolutional neural networks (CNNs) achieve state-of-the-art performance by learning from a regular grid representation of images. On…
General ClassificationGraph Classificationimage-classificationImage Classification+1Neural Pooling for Graph Neural Networks
Tasks such as graph classification, require graph pooling to learn graph-level representations from constituent node representations. In this work, we propose two novel methods using fully connected neural network layers…
ClassificationGeneral ClassificationGraph ClassificationBridging Graph Network to Lifelong Learning with Feature Interaction
Graph neural networks (GNN) are powerful models for many graph-structured tasks. In this paper, we aim to bridge GNN to lifelong learning, which is to overcome the effect of ``catastrophic forgetting" for continuously le…
Graph ClassificationLifelong learningNode ClassificationGraph Pooling by Edge Cut
Graph neural networks (GNNs) are very efficient at solving several tasks in graphs such as node classification or graph classification. They come from an adaptation of convolutional neural networks on images to graph str…
General ClassificationGraph ClassificationNode ClassificationGraph Structural Aggregation for Explainable Learning
Graph neural networks have proven to be very efficient to solve several tasks in graphs such as node classification or link prediction. These algorithms that operate by propagating information from vertices to their neig…
ClassificationGeneral ClassificationGraph Classificationimage-classification+3TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations
We present the Topology Transformation Equivariant Representation (TopoTER) learning, a general paradigm of unsupervised learning of node representations of graph data for the wide applicability to Graph Convolutional Ne…
Graph ClassificationGraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement
Graph Neural Networks (GNNs) learn effective node/graph representations by aggregating the attributes of neighboring nodes, which commonly derives a single representation mixing the information of graph structure and nod…
AttributeDisentanglementGraph ClassificationLookHops: light multi-order convolution and pooling for graph classification
Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive $k$-order($k>1$) method requires more computation cost, limiting the further application…
ClassificationGeneral ClassificationGraph ClassificationPower Normalizations in Fine-grained Image, Few-shot Image and Graph Classification
Power Normalizations (PN) are useful non-linear operators which tackle feature imbalances in classification problems. We study PNs in the deep learning setup via a novel PN layer pooling feature maps. Our layer combines …
Few-Shot LearningGeneral ClassificationGraph ClassificationMaterial Classification+1On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights
It is common practice of the outlier mining community to repurpose classification datasets toward evaluating various detection models. To that end, often a binary classification dataset is used, where samples from one of…
Binary ClassificationGeneral ClassificationGraph ClassificationGraph Embedding+1