Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Geometric Scattering for Graph Data Analysis
We explore the generalization of scattering transforms from traditional (e.g., image or audio) signals to graph data, analogous to the generalization of ConvNets in geometric deep learning, and the utility of extracted g…
General ClassificationGraph ClassificationImage ClassificationWeisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluat…
Graph ClassificationHow Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregati…
General ClassificationGraph ClassificationGraph RegressionGraph Representation Learning+3Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity …
ClusteringCombinatorial OptimizationGraph ClassificationGraph Clustering+3Exploiting Edge Features in Graph Neural Networks
Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g. graph convolutional networks (GCN) and graph attention networks (GAT), a…
General ClassificationGraph AttentionGraph ClassificationGraph Learning+2RetGK: Graph Kernels based on Return Probabilities of Random Walks
Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks. Quantifying similarities among graphs is a fundamental problem. In this paper, we develop a framework for co…
Computational EfficiencyGeneral ClassificationGraph ClassificationGraph Convolutional Neural Networks based on Quantum Vertex Saliency
This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike state-of-the-art Graph Convolutional Neu…
General ClassificationGraph ClassificationSimGNN: A Neural Network Approach to Fast Graph Similarity Computation
Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity computation, such as Graph Edit Distance (GED)…
Graph ClassificationGraph Neural NetworkGraph SimilarityAttention Models in Graphs: A Survey
Graph-structured data arise naturally in many different application domains. By representing data as graphs, we can capture entities (i.e., nodes) as well as their relationships (i.e., edges) with each other. Many useful…
Graph AttentionGraph ClassificationGraph MiningLink Prediction+1Graph Classification using Structural Attention
Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs…
ClassificationGeneral ClassificationGraph AttentionGraph ClassificationWhen Work Matters: Transforming Classical Network Structures to Graph CNN
Numerous pattern recognition applications can be formed as learning from graph-structured data, including social network, protein-interaction network, the world wide web data, knowledge graph, etc. While convolutional ne…
Graph ClassificationVideo UnderstandingHierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classificatio…
General ClassificationGraph ClassificationGraph Neural NetworkGraph Representation Learning+3GraKeL: A Graph Kernel Library in Python
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are no…
ClusteringGeneral ClassificationGraph ClassificationAnonymous Walk Embeddings
The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in g…
General ClassificationGraph ClassificationOptimal Transport for structured data with application on graphs
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space. We consider a new transportation distance (i.e. t…
ClusteringGraph ClassificationGraph ClusteringTime Series AnalysisGraph Capsule Convolutional Neural Networks
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, …
Deep LearningGeneral ClassificationGraph ClassificationLearning Graph-Level Representations with Recurrent Neural Networks
Recently a variety of methods have been developed to encode graphs into low-dimensional vectors that can be easily exploited by machine learning algorithms. The majority of these methods start by embedding the graph node…
General ClassificationGraph ClassificationChange Point Methods on a Sequence of Graphs
Given a finite sequence of graphs, e.g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process generating the …
Graph ClassificationSeizure DetectionAn End-to-End Deep Learning Architecture for Graph Classification
Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose a novel neural network architecture accepting graphs of arbitrary structure. Given a dataset containing graphs in the fo…
Deep LearningGeneral ClassificationGraph ClassificationGraph Neural NetworkWalk-Steered Convolution for Graph Classification
Graph classification is a fundamental but challenging issue for numerous real-world applications. Despite recent great progress in image/video classification, convolutional neural networks (CNNs) cannot yet cater to grap…
ClassificationClusteringGeneral ClassificationGraph Classification+1