Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Cell Attention Networks
Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relationships among nodes and then they are not able …
Graph AttentionGraph ClassificationGraph Representation LearningRepresentation LearningGraph Perceiver IO: A General Architecture for Graph Structured Data
Multimodal machine learning has been widely studied for the development of general intelligence. Recently, the remarkable multimodal algorithms, the Perceiver and Perceiver IO, show competitive results for diverse datase…
Graph ClassificationLink PredictionNode ClassificationDefending Against Backdoor Attack on Graph Nerual Network by Explainability
Backdoor attack is a powerful attack algorithm to deep learning model. Recently, GNN's vulnerability to backdoor attack has been proved especially on graph classification task. In this paper, we propose the first backdoo…
Backdoor AttackGraph ClassificationReinforced Continual Learning for Graphs
Graph Neural Networks (GNNs) have become the backbone for a myriad of tasks pertaining to graphs and similar topological data structures. While many works have been established in domains related to node and graph classi…
class-incremental learningClass Incremental LearningContinual LearningGraph Classification+1A Class-Aware Representation Refinement Framework for Graph Classification
Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect of graph-level relationships, and the g…
ClassificationGraph ClassificationGraph Representation LearningRepresentation LearningHigher-order Clustering and Pooling for Graph Neural Networks
Graph Neural Networks achieve state-of-the-art performance on a plethora of graph classification tasks, especially due to pooling operators, which aggregate learned node embeddings hierarchically into a final graph repre…
ClusteringGraph ClassificationThe PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme with Random Walks for Graph Classification
This paper presents the Persistent Weisfeiler-Lehman Random walk scheme (abbreviated as PWLR) for graph representations, a novel mathematical framework which produces a collection of explainable low-dimensional represent…
Graph ClassificationSecond-Order Global Attention Networks for Graph Classification and Regression
Graph Neural Networks (GNNs) are powerful to learn representation of graph-structured data, which fuse both attributive and topological information. Prior researches have investigated the expressive power of GNNs by comp…
ClassificationGraph ClassificationGraph RegressionregressionLearnable Filters for Geometric Scattering Modules
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS…
DescriptiveGraph ClassificationGraph Neural NetworkRevisiting Adversarial Attacks on Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in learning graph representations, current GN…
ClassificationGraph ClassificationMore Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference
Graph similarity measurement, which computes the distance/similarity between two graphs, arises in various graph-related tasks. Recent learning-based methods lack interpretability, as they directly transform interaction …
Graph ClassificationGraph SimilarityGeneralizing Downsampling from Regular Data to Graphs
Downsampling produces coarsened, multi-resolution representations of data and it is used, for example, to produce lossy compression and visualization of large images, reduce computational costs, and boost deep neural rep…
Graph ClassificationRepresentation LearningGREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Recently, graph neural networks (GNNs) have been widely used to develop successful recommender systems. Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a …
counterfactualGraph ClassificationRecommendation SystemsMaximal Independent Vertex Set applied to Graph Pooling
Convolutional neural networks (CNN) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete grid into a reduced grid with the same…
Graph Classificationimage-classificationImage ClassificationLabel-Only Membership Inference Attack against Node-Level Graph Neural Networks
Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive representations of nodes for node classification, …
Graph ClassificationInference AttackLink PredictionMembership Inference Attack+3SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
In the past few years, graph neural networks (GNNs) have become the de facto model of choice for graph classification. While, from the theoretical viewpoint, most GNNs can operate on graphs of any size, it is empirically…
Graph ClassificationGraph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search
Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Ar…
feature selectionGraph ClassificationGraph Neural NetworkGraph Property Prediction+2Wasserstein Graph Distance Based on $L_1$-Approximated Tree Edit Distance between Weisfeiler-Lehman Subtrees
The Weisfeiler-Lehman (WL) test is a widely used algorithm in graph machine learning, including graph kernels, graph metrics, and graph neural networks. However, it focuses only on the consistency of the graph, which mea…
Graph ClassificationGraph EmbeddingTREE-G: Decision Trees Contesting Graph Neural Networks
When dealing with tabular data, models based on decision trees are a popular choice due to their high accuracy on these data types, their ease of application, and explainability properties. However, when it comes to grap…
Graph ClassificationGraph LearningGraph RegressionNode ClassificationPure Transformers are Powerful Graph Learners
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat all nodes and edges as independent token…
Graph ClassificationGraph LearningGraph RegressionInductive Bias+1