• SVMlight

  • Referenced in 263 articles [sw04076]
  • Nearest Neighbor is the Spectral Graph Transducer. SVMlight can also train SVMs with cost models ... large range of problems, including text classification [Joachims, 1999c][Joachims, 1998a], image recognition tasks, bioinformatics...
  • graph2vec

  • Referenced in 6 articles [sw32340]
  • many graph analytics tasks such as graph classification and clustering require representing entire graphs ... downstream task such as graph classification, clustering and even seeding supervised representation learning approaches ... show that graph2vec achieves significant improvements in classification and clustering accuracies over substructure representation learning ... competitive with state-of-the-art graph kernels...
  • subgraph2vec

  • Referenced in 4 articles [sw36496]
  • statistical models for tasks such as graph classification, clustering, link prediction and community detection. subgraph2vec ... deep learning variant of Weisfeiler-Lehman graph kernel. Our experiments on several benchmark and large...
  • CogDL

  • Referenced in 2 articles [sw37740]
  • domain, including node classification, link prediction, graph classification, and other graph tasks. For each task...
  • GraKeL

  • Referenced in 2 articles [sw27017]
  • learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed...
  • InfoGraph

  • Referenced in 2 articles [sw37754]
  • Experimental results on the tasks of graph classification and molecular property prediction show that InfoGraph...
  • spa

  • Referenced in 4 articles [sw10496]
  • which the paper resides, and the graph is a co-citation network, with each vertex ... cite a common paper. An application involving classification of protein location ... using a protein interaction graph and an application involving classification on a manifold with part...
  • GL2vec

  • Referenced in 1 article [sw32345]
  • entire graphs which is useful for graph classification. This paper develops an algorithm which improves ... overcomes these limitations by exploiting the line graphs (edge-to-vertex dual graphs) of given ... Experimentally, GL2vec achieves significant improvements in graph classification task over Graph2vec for many benchmark datasets...
  • ktrain

  • Referenced in 2 articles [sw33372]
  • question-answering), vision data (e.g., image classification), and graph data (e.g., node classification, link prediction...
  • DropEdge

  • Referenced in 3 articles [sw37753]
  • DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. Over-fitting and over-smoothing ... obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting ... certain number of edges from the input graph at each training epoch, acting like...
  • TUDataset

  • Referenced in 1 article [sw37862]
  • this, we introduce the TUDataset for graph classification and regression. The collection consists of over ... provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools...
  • LightGCN

  • Referenced in 1 article [sw37571]
  • which is originally designed for graph classification tasks and equipped with many neural network operations ... propagating them on the user-item interaction graph, and uses the weighted...
  • RolX

  • Referenced in 7 articles [sw32343]
  • RolX: structural role extraction & mining in large graphs. Given a network, intuitively two nodes belong ... nodes, and node classification. This paper addresses the question: Given a graph...
  • Captum

  • Referenced in 3 articles [sw35075]
  • both classification and non-classification models including graph-structured models built on Neural Networks...
  • Eigen-GNN

  • Referenced in 2 articles [sw38084]
  • initial bases contain both node features and graph structures. We present extensive experimental results ... tasks including node classification, link prediction, and graph isomorphism tests...
  • KnowledgeSeeker

  • Referenced in 3 articles [sw25440]
  • ontology-based search engines, ontology-based text classification systems, ontological agent systems, semantic web systems ... defines the knowledge representation model Ontology Graph. Second, it contains an ontology learning process that ... transforms the learning outcome to the ontology graph format for machine processing, and also ... text classification) that can be carried out with the use of generated ontology graphs...
  • AFGen

  • Referenced in 17 articles [sw06325]
  • they contain. The descriptor space consists of graph fragments that can have three different types ... Experiments in the context of SVM-based classification and ranked-retrieval show that these descriptors ... analyzing the structure of the molecular graphs...
  • GBFlearn

  • Referenced in 2 articles [sw36067]
  • used for interpolation, classification and semi-supervised learning on graphs...
  • CayleyNets

  • Referenced in 4 articles [sw38090]
  • input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive ... spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification and matrix completion...
  • GraRep

  • Referenced in 13 articles [sw32342]
  • work, integrates global structural information of the graph into the learning process. We also formally ... features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation...