Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
GraphCrop: Subgraph Cropping for Graph Classification
We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification. Observing that the omission of sub-structures does not necessarily change the class label of the whole…
ClassificationData AugmentationGeneral ClassificationGraph Classification+1Contrastive Self-supervised Learning for Graph Classification
Graph classification is a widely studied problem and has broad applications. In many real-world problems, the number of labeled graphs available for training classification models is limited, which renders these models p…
ClassificationData AugmentationGeneral ClassificationGraph Classification+1Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing
Graph classification has practical applications in diverse fields. Recent studies show that graph-based machine learning models are especially vulnerable to adversarial perturbations due to the non i.i.d nature of graph …
ClassificationGeneral ClassificationGraph ClassificationGraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Normalization is known to help the optimization of deep neural networks. Curiously, different architectures require specialized normalization methods. In this paper, we study what normalization is effective for Graph Neu…
Graph ClassificationGraph Neural NetworkGraph Representation LearningRepresentation LearningLifelong Graph Learning
Graph neural networks (GNN) are powerful models for many graph-structured tasks. Existing models often assume that the complete structure of the graph is available during training. In practice, however, graph-structured …
Action RecognitionContinual LearningGraph ClassificationGraph Learning+3Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
Graph convolutional neural networks (GCNNs) have received much attention recently, owing to their capability in handling graph-structured data. Among the existing GCNNs, many methods can be viewed as instances of a neura…
Data AugmentationGraph ClassificationComplete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood node information. Though effective for various tasks, in thi…
Graph ClassificationNode ClassificationQuaternion Graph Neural Networks
Recently, graph neural networks (GNNs) have become an important and active research direction in deep learning. It is worth noting that most of the existing GNN-based methods learn graph representations within the Euclid…
General ClassificationGraph ClassificationGraph EmbeddingKnowledge Graph Completion+4PiNet: Attention Pooling for Graph Classification
We propose PiNet, a generalised differentiable attention-based pooling mechanism for utilising graph convolution operations for graph level classification. We demonstrate high sample efficiency and superior performance o…
ClassificationGeneral ClassificationGraph ClassificationDegree-Quant: Quantization-Aware Training for Graph Neural Networks
Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring method…
CPUGraph ClassificationGraph RegressionNode Classification+1Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription
We present an end-to-end, interpretable, deep-learning architecture to learn a graph kernel that predicts the outcome of chronic disease drug prescription. This is achieved through a deep metric learning collaborative wi…
Graph ClassificationMetric LearningTripletDetecting Beneficial Feature Interactions for Recommender Systems
Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features. However, this is suboptimal because some feature inter…
Graph ClassificationGraph Neural NetworkRecommendation SystemsMulti-view adaptive graph convolutions for graph classification
In this paper, a novel multi-view methodology for graph-based neural networks is proposed. A systematic and methodological adaptation of the key concepts of classical deep learning methods such as convolution, pooling an…
ClassificationGeneral ClassificationGraph ClassificationGraph Neural Network+1MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
Graph Neural Networks (GNNs) have recently caught great attention and achieved significant progress in graph-level applications. In this paper, we propose a framework for graph neural networks with multiresolution Haar-l…
Graph ClassificationSecond-Order Pooling for Graph Neural Networks
Graph neural networks have achieved great success in learning node representations for graph tasks such as node classification and link prediction. Graph representation learning requires graph pooling to obtain graph rep…
Graph ClassificationGraph Representation LearningLink PredictionNode Classification+1Robust Hierarchical Graph Classification with Subgraph Attention
Graph neural networks get significant attention for graph representation and classification in machine learning community. Attention mechanism applied on the neighborhood of a node improves the performance of graph neura…
ClassificationGeneral ClassificationGraph ClassificationTUDataset: A collection of benchmark datasets for learning with graphs
Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation proc…
Graph ClassificationGraph Neural NetworkregressionM-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification
Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc. However, the limitation of scale in the benchmark dat…
ClassificationData AugmentationGeneral ClassificationGraph ClassificationMultilevel Graph Matching Networks for Deep 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 the task of graph similarity learning. Recent work on gra…
Graph ClassificationGraph MatchingGraph Neural NetworkGraph Regression+2Simple and Deep Graph Convolutional Networks
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datas…
Graph ClassificationGraph RegressionLink PredictionNode Classification+2