Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Kernel Graph Convolutional Neural Nets
Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel. Th…
Graph ClassificationDGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model
Convolutional neural networks (CNNs) can be applied to graph similarity matching, in which case they are called graph CNNs. Graph CNNs are attracting increasing attention due to their effectiveness and efficiency. Howeve…
Graph ClassificationGraph SimilarityRetrievalHunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
For the purpose of learning on graphs, we hunt for a graph feature representation that exhibit certain uniqueness, stability and sparsity properties while also being amenable to fast computation. This leads to the discov…
General ClassificationGraph ClassificationSplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution ope…
Deep LearningGeneral ClassificationGraph ClassificationNode Classification+1Residual Gated Graph ConvNets
Graph-structured data such as social networks, functional brain networks, gene regulatory networks, communications networks have brought the interest in generalizing deep learning techniques to graph domains. In this pap…
ClusteringGeneral ClassificationGraph ClassificationGraph Clustering+3Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph …
Document ClassificationGraph AttentionGraph ClassificationGraph Embedding+10Kernel Graph Convolutional Neural Networks
Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel. Th…
General ClassificationGraph ClassificationDeep Graph Attention Model
Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph features. In a graphlet-based approach, for …
General ClassificationGraph AttentionGraph ClassificationmodelLearning Universal Adversarial Perturbations with Generative Models
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. It was recently shown that …
Graph ClassificationGraph Classification via Deep Learning with Virtual Nodes
Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix). Such a representation works nicely with algebraic manipulations. Here we introduce a simple method to aug…
ClassificationDeep LearningGeneral ClassificationGraph ClassificationGraph Classification with 2D Convolutional Neural Networks
Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs…
ClassificationGeneral ClassificationGraph ClassificationGraph Learninggraph2vec: Learning Distributed Representations of Graphs
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such a…
ClusteringGeneral ClassificationGraph ClassificationGraph Embedding+2Kernel method for persistence diagrams via kernel embedding and weight factor
Topological data analysis is an emerging mathematical concept for characterizing shapes in multi-scale data. In this field, persistence diagrams are widely used as a descriptor of the input data, and can distinguish robu…
Graph ClassificationTopological Data AnalysisSliced Wasserstein Kernel for Persistence Diagrams
Persistence diagrams (PDs) play a key role in topological data analysis (TDA), in which they are routinely used to describe topological properties of complicated shapes. PDs enjoy strong stability properties and have pro…
Graph ClassificationTopological Data AnalysisInductive Representation Learning on Large Graphs
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require t…
Graph ClassificationGraph RegressionLink PredictionLink Property Prediction+4Dynamics Based Features For Graph Classification
Numerous social, medical, engineering and biological challenges can be framed as graph-based learning tasks. Here, we propose a new feature based approach to network classification. We show how dynamics on a network can …
ClassificationGeneral ClassificationGraph ClassificationSupervised Community Detection with Line Graph Neural Networks
Traditionally, community detection in graphs can be solved using spectral methods or posterior inference under probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent …
Community DetectionGraph ClassificationStochastic Block ModelDynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
A number of problems can be formulated as prediction on graph-structured data. In this work, we generalize the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows …
3D Object Classification3D Point Cloud ClassificationClassificationGeneral Classification+2Modeling Relational Data with Graph Convolutional Networks
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia o…
DecoderGeneral ClassificationGraph ClassificationHeterogeneous Node Classification+7Shift Aggregate Extract Networks
We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested part-of-pa…
General ClassificationGraph Classification