• MCODE

  • Referenced in 48 articles [sw35748]
  • Results: This paper describes a novel graph theoretic clustering algorithm, ”Molecular Complex Detection” (MCODE), that ... algorithm has the advantage over other graph clustering methods of having a directed mode that...
  • flexclust

  • Referenced in 27 articles [sw04583]
  • visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap...
  • SymNMF

  • Referenced in 11 articles [sw12668]
  • approximation of a similarity matrix for graph clustering. Nonnegative matrix factorization (NMF) provides a lower ... study an extended formulation for graph clustering called Symmetric NMF (SymNMF). In contrast ... clustering, justify SymNMF as a general graph clustering method, and discuss the strengths and shortcomings ... image segmentation support SymNMF as a graph clustering method that captures latent linear and nonlinear...
  • SA-cluster

  • Referenced in 10 articles [sw06867]
  • networks, sensor networks, biological networks, etc. Graph clustering has shown its effectiveness in analyzing ... visualizing large networks. The goal of graph clustering is to partition vertices in a large ... connectivity or neighborhood similarity. Many existing graph clustering methods mainly focus on the topological structures ... often heterogeneous. Recently, a new graph clustering algorithm, SA-cluster, has been proposed which combines...
  • Tulip

  • Referenced in 15 articles [sw07464]
  • most important capabilities of a graph visualization software called Tulip. This software has been developed ... order to experiment tools such as clustering, graph drawing and metrics coloring for the purpose ... Tulip’s characteristics are: a graph model which allows clustering with data sharing ... written in C++ and uses the Tulip graph library, the OpenGL library...
  • KeyGraph

  • Referenced in 8 articles [sw24377]
  • forward in order to cluster the characteristics of human behavior, so as to detect ... realized through establishing KeyGraph and employing graph cluster algorithms. Firstly, the multi-dimension behavior features ... noise data were filtered and the undirected graph based on the characteristics ... human behavior was established. Finally graph-clustering algorithm SCAN was applied to find...
  • Inc-cluster

  • Referenced in 5 articles [sw06866]
  • networks, sensor networks, biological networks, etc. Graph clustering has shown its effectiveness in analyzing ... visualizing large networks. The goal of graph clustering is to partition vertices in a large ... connectivity or neighborhood similarity. Many existing graph clustering methods mainly focus on the topological structures ... often heterogeneous. Recently, a new graph clustering algorithm, SA-cluster, has been proposed which combines...
  • PSPIKE

  • Referenced in 16 articles [sw07072]
  • systems, real, parallel on distributed-memory clusters, combinatorial graph algorithms...
  • graph2vec

  • Referenced in 5 articles [sw32340]
  • graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed ... downstream task such as graph classification, clustering and even seeding supervised representation learning approaches ... graph2vec achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches ... competitive with state-of-the-art graph kernels...
  • CLUSTAG

  • Referenced in 7 articles [sw30040]
  • CLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs. Summary: Cluster and set-cover...
  • JUNG

  • Referenced in 11 articles [sw12112]
  • analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation...
  • Apache Spark

  • Referenced in 41 articles [sw28418]
  • Spark: Spark is a fast and general cluster computing system for Big Data. It provides ... optimized engine that supports general computation graphs for data analysis. It also supports a rich...
  • mixer

  • Referenced in 4 articles [sw12288]
  • package mixer: Random graph clustering. Routines for the analysis (unsupervised clustering) of networks using MIXtures...
  • ProClust

  • Referenced in 6 articles [sw35761]
  • ProClust: improved clustering of protein sequences with an extended graph ... based approach. Results: We extend a graph-based clustering algorithm which uses an asymmetric distance...
  • MapSets

  • Referenced in 4 articles [sw13776]
  • MapSets: visualizing embedded and clustered graphs. In addition to objects and relationships between them, groups...
  • IsoRankN

  • Referenced in 10 articles [sw08325]
  • alignment tool based on spectral clustering on the induced graph of pairwise alignment scores. IsoRankN...
  • LAMG

  • Referenced in 26 articles [sw06551]
  • graphs arise in large-scale computational applications such as semisupervised machine learning; spectral clustering ... presented, where $A$ is a graph Laplacian. LAMG’s run time and storage are empirically...
  • CGV

  • Referenced in 3 articles [sw19315]
  • been used to evaluate graph clustering results, to navigate topological structures of neuronal systems ... perform analysis of some time-varying graphs...
  • Pregel

  • Referenced in 33 articles [sw13416]
  • that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible ... efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers ... result is a framework for processing large graphs that is expressive and easy to program...
  • subgraph2vec

  • Referenced in 4 articles [sw36496]
  • statistical models for tasks such as graph classification, clustering, link prediction and community detection. subgraph2vec ... such as CNNs, SVMs and relational data clustering algorithms to achieve significantly superior accuracies. Also ... deep learning variant of Weisfeiler-Lehman graph kernel. Our experiments on several benchmark and large...