Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Cluster Computing
Published in: Cluster Computing 3/2019

25-11-2017

Research on subgraph distribution algorithm based on label null model

Author: Zhisong Wang

Published in: Cluster Computing | Special Issue 3/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In view of the problem of low accuracy of subgraph distribution algorithm in existing graph classification, this paper constructs a n-order label null model based on the n-order null model, and proposes an index algorithm Build Graph Location Index (BGLI) and a subgraph distribution algorithm estimate subgraph on spark (ESGS). Firstly, in order to add the classification characteristics of the graph, the topological structure information, the vertex and edge label information of the graph are taken into consideration, and the n-order label null model is constructed. And validity that the label null model is used for the classification is proved. Secondly, in order to improve the efficiency of subgraph retrieval, BGLI algorithm is proposed to build the index based on label null model. Then, based on BGLI algorithm, the ESGS algorithm is proposed. Finally, the experiments prove that the subgraph extracted by ESGS algorithm as the classification characteristics can improve the accuracy of the classification.
previous article Research on visual 3D assembly process design and simulation for marine diesel engine
next article Operational modal analysis of rigid frame bridge with data from navigation satellite system measurements

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Lam, W.W.M., Chan, K.C.C.: A graph mining algorithm for classifying chemical compounds. In: IEEE International Conference on Bioinformatics and Biomedicine, Philadelphia, Pennsylvania, USA, pp. 321–324 (2008)
2.
Pan, S., Wu, J., Zhu, X., et al.: Finding the best not the most: regularized loss minimization subgraph selection for graph classification. Pattern Recogn. 48(11), 3783–3796 (2015)CrossRef
3.
Lai, L., Qin, L., Lin, X., et al.: Scalable distributed subgraph enumeration. Proc. VLDB Endow. 10(3), 217–228 (2016)CrossRef
4.
He, Y., Wang, T., Xie, J., et al.: Parallel frequent subgraph mining algorithm. In: Proceedings of the International Conference on Software and Computer Applications. ACM pp. 98–202 (2017)
5.
Pan, S., Wu, J., Zhu, X.: CogBoost: boosting for fast cost-sensitive graph classification. IEEE Trans. Knowl. Data Eng. 27(11), 2933–2946 (2015)CrossRef
6.
Przulj, N., Corneil, D.I.: Modeling interactome scale-free or geometric. Bioinformatics 20(18), 3508–3515 (2004)CrossRef
7.
Milenković, T., Przulj, N.: Uncovering biological network function via graphlet degree signatures. Cancer Inform. 6(1), 257–273 (2008)
8.
Ahmed, N.K., Neville, J., Rossi, R.A., et al.: Efficient graphlet counting for large networks. In: International Conference on Data Mining series, Atlantic, USA, pp. 1–10 (2015)
9.
Elenberg, E.R., Shanmugam, K., Borokhovich, M., et al.: Beyond triangles a distributed framework for estimating 3-profiles of large graphs. In: International Conference on Knowledge Discovery and Data Mining. Sydney, Australia, pp. 229–238 (2015)
10.
Elenberg, E.R., Shanmugam, K., Borokhovich, M., et al.: Distributed estimation of graph 4-profiles. In: International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 483–493 (2016)
11.
Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: European Conference on Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, pp. 13–23 (2000)CrossRef
12.
Yan, X., Han, J.: Gspan: Graph-based substructure pattern mining. In: International Conference on Data Mining series, Melbourne, Florida, USA, pp. 721–724 (2003)
13.
Wang, C., Wang, W., Pei, J., et al.: Scalable mining of large disk-based graph databases. In: International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, August., pp. 316–325 (2004)
14.
Bringmann, B., Nijssen, S.: What is Frequent in a Single Graph? In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, pp. 858–863 (2008)
15.
Elseidy, M., Abdelhamid, E., Skiadopoulos, S., et al.: Grami: frequent subgraph and pattern mining in a single large graph. Proc. VLDB Endow. 7(7), 517–528 (2014)CrossRef
16.
Fan, W., Wang, X., Wu, Y., et al.: Association rules with graph patterns. Proc. VLDB Endow. 8(12), 1502–1513 (2015)CrossRef
17.
Chen, Y., Zhao, X., Lin, X., et al.: Towards frequent subgraph mining on single large uncertain graphs. In: International Conference on Data Mining. Miami, USA, pp. 41–50 (2015)
18.
Dong, G., Yang, W., Zhu, F., et al.: Discovering burst patterns of burst topic in Twitter. Comput. Electr. Eng. 22(10), 426–440 (2016)
19.
Fang, Y., Cheng, R., Luo, S., et al.: Effective community search for large attributed graphs. Proc. VLDB Endow. 9(12), 1233–1244 (2016)CrossRef
20.
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 3rd edn. China Machine Press, Beijing (2012)MATH
21.
Jing, Y., Han, Y.: URSI: high efficient query algorithm on subgraph isomorphism. J. Yanshan Univ. 40(6), 517–523 (2016)
22.
23.
Gjoka, M., Kurant, M., Markopoulou, A.: 2.5K-graphs: from sampling to generation. In: IEEE INFOCOM. New York, USA, pp. 1968–1976 (2013)
24.
Mahadevan, P., Hubble, C., Krioukov, D., et al.: Orbis: rescaling degree correlations to generate annotated internet topologies. ACM SIGCOMM Comput. Commun. Rev. 37(4), 325–336 (2007)CrossRef
25.
Mahadevan, P., Krioukov, D., Fall, K., et al.: Systematic topology analysis and generation using degree correlations. ACM SIGCOMM Comput. Commun. Rev. 36(4), 135–146 (2006)CrossRef
26.
Holme, P., Kim, B.J.: Growing scale-free networks with tunable clustering. Phys. Rev. E 65(2), 026107 (2002)CrossRef
Metadata
Title
Research on subgraph distribution algorithm based on label null model
Author
Zhisong Wang
Publication date
25-11-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1359-5

Other articles of this Special Issue 3/2019

Cluster Computing 3/2019 Go to the issue

OriginalPaper

Improved canny detection algorithm for processing and segmenting text from the images

OriginalPaper

Operational modal analysis of rigid frame bridge with data from navigation satellite system measurements

OriginalPaper

Research on networked control system based on T-S model

OriginalPaper

Research on classification and recognition of attacking factors based on radial basis function neural network

OriginalPaper

Survey of access control models and technologies for cloud computing

OriginalPaper

A novel online incremental and decremental learning algorithm based on variable support vector machine

Premium Partner

    Image Credits