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Volumn , Issue , 2011, Pages 652-663

On classification of graph streams

Author keywords

Classification; Graphs

Indexed keywords

CLASSIFICATION (OF INFORMATION); GRAPHIC METHODS;

EID: 84874028089     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.56     Document Type: Conference Paper
Times cited : (42)

References (21)
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    • XProj: A framework for projected structural clustering of XML documents
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    • Aggarwal, C.1    Ta, N.2    Feng, J.3    Wang, J.4    Zaki, M.J.5
  • 7
    • 33745790274 scopus 로고    scopus 로고
    • Graph mining: Laws, generators and algorithms
    • D. Chakrabarti, and C. Faloutsos, Graph Mining: Laws, Generators and Algorithms, ACM Computing Surveys, 38(1), (2006).
    • (2006) ACM Computing Surveys , vol.38 , Issue.1
    • Chakrabarti, D.1    Faloutsos, C.2
  • 11
    • 33745630997 scopus 로고    scopus 로고
    • Discovering large dense subgraphs in massive graphs
    • D. Gibson, R. Kumar, and A. Tomkins, Discovering Large Dense Subgraphs in Massive Graphs, VLDB Conference, (2005).
    • (2005) VLDB Conference
    • Gibson, D.1    Kumar, R.2    Tomkins, A.3
  • 12
    • 49749125569 scopus 로고    scopus 로고
    • Efficient algorithms for mining significant substructures in large graphs with quality guarantees
    • H. He, and A. K. Singh, Efficient Algorithms for Mining Significant Substructures in Large Graphs with Quality Guarantees, ICDM Conference, (2007).
    • (2007) ICDM Conference
    • He, H.1    Singh, A.K.2
  • 14
    • 30344471433 scopus 로고    scopus 로고
    • An application of boosting to graph classification
    • T. Kudo, E. Maeda, and Y. Matsumoto, An Application of Boosting to Graph Classification, NIPS Conf. (2004).
    • (2004) NIPS Conf.
    • Kudo, T.1    Maeda, E.2    Matsumoto, Y.3
  • 16
    • 67649644367 scopus 로고    scopus 로고
    • GraphSig: A scalable approach to mining significant subgraphs in large graph databases
    • S. Ranu, and A. K. Singh, GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases, ICDE Conference, (2009).
    • (2009) ICDE Conference
    • Ranu, S.1    Singh, A.K.2
  • 17
    • 49549119318 scopus 로고    scopus 로고
    • Graph clustering with network sructure indices
    • M. Rattigan, M. Maier, and D. Jensen, Graph Clustering with Network Sructure Indices. ICML, (2007).
    • (2007) ICML
    • Rattigan, M.1    Maier, M.2    Jensen, D.3
  • 18
    • 77952415079 scopus 로고    scopus 로고
    • Mining concept-drifting data streams using ensemble classifiers
    • H. Wang, W. Fan, P. S. Yu, and J. Han, Mining Concept-Drifting Data Streams using Ensemble Classifiers, ACM KDD Conference, (2003).
    • (2003) ACM KDD Conference
    • Wang, H.1    Fan, W.2    Yu, P.S.3    Han, J.4
  • 19
    • 33749559199 scopus 로고    scopus 로고
    • Suppressing model overfitting in mining concept-drifting data streams
    • H. Wang, J. Yin, J. Pei, P. S. Yu, and J. X. Yu, Suppressing Model Overfitting in Mining Concept-Drifting Data Streams, ACM KDD Conference, (2006).
    • (2006) ACM KDD Conference
    • Wang, H.1    Yin, J.2    Pei, J.3    Yu, P.S.4    Yu, J.X.5
  • 20
    • 77952334885 scopus 로고    scopus 로고
    • CloseGraph: Mining closed frequent graph patterns
    • X. Yan, and J. Han, CloseGraph: Mining Closed Frequent Graph Patterns, ACM KDD Conference, (2003).
    • (2003) ACM KDD Conference
    • Yan, X.1    Han, J.2
  • 21
    • 77952397753 scopus 로고    scopus 로고
    • XRules: An effective structural classifier for XML data
    • M. J. Zaki, and C. C. Aggarwal, XRules: An Effective Structural Classifier for XML Data, KDD Conf. (2003).
    • (2003) KDD Conf.
    • Zaki, M.J.1    Aggarwal, C.C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.