• gSpan

  • Referenced in 115 articles [sw11908]
  • gSpan: graph-based substructure pattern mining. We investigate ... approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm ... called gSpan (graph-based substructure pattern mining), which discovers frequent substructures without candidate generation. gSpan ... builds a new lexicographic order among graphs, and maps each graph to a unique minimum...
  • CloseGraph

  • Referenced in 31 articles [sw37017]
  • CloseGraph: mining closed frequent graph patterns. Recent research on pattern discovery has progressed form mining ... bioinformatics, Web exploration, and etc. However, mining large graph patterns in challenging ... subgraphs, we propose to mine closed frequent graph patterns. A graph g is closed ... same support as g. A closed graph pattern mining algorithm, CloseGraph, is developed by exploring...
  • ANF

  • Referenced in 14 articles [sw12276]
  • scalable tool for data mining in massive graphs. Graphs are an increasingly important data source ... represented as a graph. This work presents a data mining tool, called ANF, that ... present some of our results from mining large graphs using...
  • PEGASUS

  • Referenced in 8 articles [sw17479]
  • PEGASUS: A peta-scale graph mining system implementation and observations. In this paper, we describe ... PEGASUS, an open source Peta Graph Mining library which performs typical graph mining tasks such ... open source version of MapReduce. Many graph mining operations (PageRank, spectral clustering, diameter estimation, connected ... report our findings on several real graphs, including one of the largest publicly available...
  • DOULION

  • Referenced in 12 articles [sw30192]
  • triangle counting algorithm. Furthermore, several interesting graph mining applications rely on computing the number...
  • gBoost

  • Referenced in 8 articles [sw42199]
  • approach to graph classification and regression. Graph mining methods enumerate frequently appearing subgraph patterns, which ... iterations. To apply the boosting method to graph data, a branch-and-bound pattern search ... simpler method based on frequent substructure mining, because the output labels are used...
  • GraphChi

  • Referenced in 5 articles [sw34198]
  • well-known method to break large graphs into small parts, and a novel parallel sliding ... able to execute several advanced data mining, graph mining, and machine learning algorithms on very...
  • GraphScope

  • Referenced in 25 articles [sw20427]
  • GraphScope: Parameter-free Mining of Large Time-evolving Graphs. How can we find communities...
  • JUNG

  • Referenced in 11 articles [sw12112]
  • number of algorithms from graph theory, data mining, and social network analysis, such as routines...
  • RolX

  • Referenced in 8 articles [sw32343]
  • RolX: structural role extraction & mining in large graphs. Given a network, intuitively two nodes belong ... Roles enable numerous novel and useful network-mining tasks, such as sense-making, searching ... This paper addresses the question: Given a graph, how can we automatically discover roles ... effectiveness of RolX on several network-mining tasks: from exploratory data analysis to network transfer...
  • AFGen

  • Referenced in 22 articles [sw06325]
  • they contain. The descriptor space consists of graph fragments that can have three different types ... descriptors obtained by mining and analyzing the structure of the molecular graphs...
  • Naiad

  • Referenced in 4 articles [sw32529]
  • erative machine learning, and interactive graph mining. Naiad outperforms specialized systems in their target...
  • Gplag

  • Referenced in 7 articles [sw08961]
  • detection of software plagiarism by program dependence graph analysis. Along with the blossom of open ... GPLAG, which detects plagiarism by mining program dependence graphs (PDGs). A PDG is a graphic...
  • Karate Club

  • Referenced in 2 articles [sw32339]
  • than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning ... make community detection, node and whole graph embedding available to a wide audience of machine...
  • Colibri

  • Referenced in 9 articles [sw12043]
  • Colibri: fast mining of large static and dynamic graphs. Low-rank approximations of the adjacency...
  • XSnippet

  • Referenced in 5 articles [sw21710]
  • XSnippet: mining For sample code. It is common practice for software developers to use examples ... instantiation queries. Second, a novel graph-based code mining algorithm is provided to support...
  • SIAS-miner

  • Referenced in 3 articles [sw38343]
  • Data clustering, local pattern mining, and community detection in graphs are three mature areas ... powerful data mining task in the intersection of these areas. Given a graph ... attributes for each vertex, attributed subgraph mining aims to find cohesive subgraphs for which (some ... have exceptional values. The principled integration of graph and attribute data poses two challenges...
  • ObliVM

  • Referenced in 5 articles [sw29146]
  • showcase applications such as data mining, streaming algorithms, graph algorithms, genomic data analysis, and data...
  • persona2vec

  • Referenced in 1 article [sw33469]
  • performance in many graph mining tasks. Most existing embedding algorithms assign a single vector ... contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations ... performance in many graph mining tasks. Most existing embedding algorithms assign a single vector ... contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations...
  • MC73

  • Referenced in 7 articles [sw12394]
  • that include matrix reordering, graph partitioning, protein analysis, data mining, machine learning, and web search ... computing the Fiedler vector of large graphs based on the Trace Minimization algorithm. We compare...