메뉴 건너뛰기




Volumn , Issue , 2012, Pages 439-450

PICS: Parameter-free identification of cohesive subgroups in large attributed graphs

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; GENE EXPRESSION; PARAMETER ESTIMATION; SPACE DIVISION MULTIPLE ACCESS; STATISTICS;

EID: 84880243365     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972825.38     Document Type: Conference Paper
Times cited : (123)

References (30)
  • 1
    • 84958161527 scopus 로고    scopus 로고
    • The political blogosphere and the 2004 u.s. Election: Divided they blog
    • L. Adamic and N. Glance. The political blogosphere and the 2004 u.s. election: Divided they blog. In LinkKDD, pages 36-43, 2005.
    • (2005) LinkKDD , pp. 36-43
    • Adamic, L.1    Glance, N.2
  • 2
    • 38749085661 scopus 로고    scopus 로고
    • Local graph partitioning using pagerank vectors
    • R. Andersen, F. Chung, and K. Lang. Local graph partitioning using pagerank vectors. In FOCS, pages 475-486, 2006.
    • (2006) FOCS , pp. 475-486
    • Andersen, R.1    Chung, F.2    Lang, K.3
  • 4
    • 51349089444 scopus 로고    scopus 로고
    • Autopart: Parameter-free graph partitioning and outlier detection
    • D. Chakrabarti. Autopart: Parameter-free graph partitioning and outlier detection. In PKDD, 2004.
    • (2004) PKDD
    • Chakrabarti, D.1
  • 6
    • 0027211363 scopus 로고
    • Spectral k-way ratio-cut partitioning and clustering
    • P. K. Chan, M. D. F. Schlag, and J. Y. Zien. Spectral k-way ratio-cut partitioning and clustering. In DAC, pages 749-754, 1993.
    • (1993) DAC , pp. 749-754
    • Chan, P.K.1    Schlag, M.D.F.2    Zien, J.Y.3
  • 7
    • 77952375075 scopus 로고    scopus 로고
    • Informationtheoretic co-clustering
    • I. Dhillon, S. Mallela, and D. Modha. Informationtheoretic co-clustering. In KDD, 2003.
    • (2003) KDD
    • Dhillon, I.1    Mallela, S.2    Modha, D.3
  • 8
    • 78149301227 scopus 로고    scopus 로고
    • A min-max cut algorithm for graph partitioning and data clustering
    • C. H. Q. Ding, X. He, H. Zha, M. Gu, and H. D. Simon. A min-max cut algorithm for graph partitioning and data clustering. In ICDM, 2001.
    • (2001) ICDM
    • Ding, C.H.Q.1    He, X.2    Zha, H.3    Gu, M.4    Simon, H.D.5
  • 9
    • 67249107496 scopus 로고    scopus 로고
    • Inferring social network structure using mobile phone data
    • N. Eagle, A. Pentland, and D. Lazer. Inferring social network structure using mobile phone data. PNAS, 2007.
    • (2007) PNAS
    • Eagle, N.1    Pentland, A.2    Lazer, D.3
  • 10
    • 0034592749 scopus 로고    scopus 로고
    • Efficient identification of web communities
    • G. W. Flake, S. Lawrence, and C. L. Giles. Efficient identification of web communities. In KDD. 2000.
    • (2000) KDD.
    • Flake, G.W.1    Lawrence, S.2    Giles, C.L.3
  • 11
    • 0037062448 scopus 로고    scopus 로고
    • Community structure in social and biological networks
    • M. Girvan and M. E. J. Newman. Community structure in social and biological networks. Proc. of Nat. Acad. of Sci., 99(12):7821-7826, 2002.
    • (2002) Proc. of Nat. Acad. of Sci. , vol.99 , Issue.12 , pp. 7821-7826
    • Girvan, M.1    Newman, M.E.J.2
  • 13
    • 79951736796 scopus 로고    scopus 로고
    • Subspace clustering meets dense subgraph mining: A synthesis of two paradigms
    • S. Gunnemann, I. Farber, B. Boden, and T. Seidl. Subspace clustering meets dense subgraph mining: A synthesis of two paradigms. In ICDM, 2010.
    • (2010) ICDM
    • Gunnemann, S.1    Farber, I.2    Boden, B.3    Seidl, T.4
  • 14
    • 0038014879 scopus 로고    scopus 로고
    • Coclustering of biological networks and gene expression data
    • D. Hanisch, A. Zien, R. Zimmer, and T. Lengauer. Coclustering of biological networks and gene expression data. In ISMB, pages 145-154, 2002.
    • (2002) ISMB , pp. 145-154
    • Hanisch, D.1    Zien, A.2    Zimmer, R.3    Lengauer, T.4
  • 17
    • 0009007122 scopus 로고    scopus 로고
    • Multilevel algorithms for multi-constraint graph partitioning
    • G. Karypis and V. Kumar. Multilevel algorithms for multi-constraint graph partitioning. In Proc. of Supercomputing, pages 1-13, 1998.
    • (1998) Proc. of Supercomputing , pp. 1-13
    • Karypis, G.1    Kumar, V.2
  • 18
    • 84904201651 scopus 로고    scopus 로고
    • Clustering high-dimensional data: A survey
    • H.-P. Kriegel, P. Kroger, and A. Zimek. Clustering high-dimensional data: A survey. ACM TKDD, 3(1):1-58, 2009.
    • (2009) ACM TKDD , vol.3 , Issue.1 , pp. 1-58
    • Kriegel, H.-P.1    Kroger, P.2    Zimek, A.3
  • 19
    • 34250765347 scopus 로고    scopus 로고
    • Spectral clustering for multi-type relational data
    • B. Long, Z. Zhang, X. Wu, and P. S. Yu. Spectral clustering for multi-type relational data. In ICML, volume 148, pages 585-592, 2006.
    • (2006) ICML , vol.148 , pp. 585-592
    • Long, B.1    Zhang, Z.2    Wu, X.3    Yu, P.S.4
  • 21
    • 74549169516 scopus 로고    scopus 로고
    • Mining cohesive patterns from graphs with feature vectors
    • F. Moser, R. Colak, A. Raey, and M. Ester. Mining cohesive patterns from graphs with feature vectors. In SDM, pages 593-604, 2009.
    • (2009) SDM , pp. 593-604
    • Moser, F.1    Colak, R.2    Raey, A.3    Ester, M.4
  • 22
    • 0041875229 scopus 로고    scopus 로고
    • On spectral clustering: Analysis and an algorithm
    • A. Y. Ng, M. I. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In NIPS, 2001.
    • (2001) NIPS
    • Ng, A.Y.1    Jordan, M.I.2    Weiss, Y.3
  • 23
    • 0001098776 scopus 로고
    • A universal prior for integers and estimation by minimum description length
    • J. Rissanen. A universal prior for integers and estimation by minimum description length. The Annals of Statistics, 11(2):416-431, 1983.
    • (1983) The Annals of Statistics , vol.11 , Issue.2 , pp. 416-431
    • Rissanen, J.1
  • 25
    • 36849035825 scopus 로고    scopus 로고
    • Graphscope: Parameter-free mining of large timeevolving graphs
    • J. Sun, C. Faloutsos, S. Papadimitriou, and P. S. Yu. Graphscope: parameter-free mining of large timeevolving graphs. In KDD, pages 687-696, 2007.
    • (2007) KDD , pp. 687-696
    • Sun, J.1    Faloutsos, C.2    Papadimitriou, S.3    Yu, P.S.4
  • 26
    • 57149123533 scopus 로고    scopus 로고
    • Efficient aggregation for graph summarization
    • Y. Tian, R. A. Hankins, and J. M. Patel. Efficient aggregation for graph summarization. In SIGMOD, pages 567-580, 2008.
    • (2008) SIGMOD , pp. 567-580
    • Tian, Y.1    Hankins, R.A.2    Patel, J.M.3
  • 27
    • 84880192548 scopus 로고    scopus 로고
    • Book citations on U.S. politics
    • http://www-personal.umich.edu/~mejn/netdata/Book citations on U.S. politics.
  • 29
    • 77955045035 scopus 로고    scopus 로고
    • Graph clustering based on structural/attribute similarities
    • Y. Zhou, H. Cheng, and J. X. Yu. Graph clustering based on structural/attribute similarities. PVLDB, 2(1):718-729, 2009.
    • (2009) PVLDB , vol.2 , Issue.1 , pp. 718-729
    • Zhou, Y.1    Cheng, H.2    Yu, J.X.3
  • 30
    • 79951739260 scopus 로고    scopus 로고
    • Clustering large attributed graphs: An efficient incremental approach
    • Y. Zhou, H. Cheng, and J. X. Yu. Clustering large attributed graphs: An efficient incremental approach. In ICDM, pages 689-698, 2010.
    • (2010) ICDM , pp. 689-698
    • Zhou, Y.1    Cheng, H.2    Yu, J.X.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.