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
Published in:
A Targeted Retraining Scheme of Unsupervised Word Embeddings for Specific Supervised Tasks Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Centrality-Based Local-First Approach for Analyzing Overlapping Communities in Dynamic Networks

Authors : Ximan Chen, Heli Sun, Hongxia Du, Jianbin Huang, Ke Liu

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

With the increasing demand of dynamic graph data analysis, mining communities in time-evolving data has been a research hotspot. However, traditional community detection methods have efficiency issue in the huge dynamic network data and rarely consider about overlapping communities. In this paper, we first propose a centrality-based local-first approach for overlapping community discovery in static network, called CBLF. Different with the traditional top-down approach, CBLF detects communities from central nodes and theirs neighbors which conforms to reality better. Then we present a novel evolutionary community detection approach called CBLFD based on this effective approach and sequence smoothing mechanism. Experimental results on real-world and synthetic datasets demonstrate that these algorithms achieve higher accuracy and efficiency compared with the state-of-art algorithms.

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
previous chapter Self-tuning Filers — Overload Prediction and Preventive Tuning Using Pruned Random Forest
next chapter Web-Scale Personalized Real-Time Recommender System on Suumo
Footnotes
Literature
1.
Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)CrossRef
2.
Cazabet, R., Amblard, F., Hanachi, C.: Detection of overlapping communities in dynamical social networks. In: Proceedings of the 2nd International Conference on Social Computing (SocialCom), pp. 309–314. IEEE (2010)
3.
Chi, Y., Song, X., Zhou, D., Hino, K., Tseng, B.L.: Evolutionary spectral clustering by incorporating temporal smoothness. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 153–162. ACM (2007)
4.
Greene, D., Doyle, D., Cunningham, P.: Tracking the evolution of communities in dynamic social networks. In: Proceedings of the 2010 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 176–183. IEEE (2010)
5.
Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)CrossRef
6.
Kim, M.S., Han, J.: A particle-and-density based evolutionary clustering method for dynamic networks. Proc. VLDB Endowment 2(1), 622–633 (2009)CrossRef
7.
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033015 (2009)CrossRef
8.
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)CrossRef
9.
Nguyen, N.P., Dinh, T.N., Tokala, S., Thai, M.T.: Overlapping communities in dynamic networks: their detection and mobile applications. In: Proceedings of the 17th Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 85–96. ACM (2011)
10.
Nicosia, V., Mangioni, G., Malgeri, M., Carchiolo, V.: Extending modularity definition for directed graphs with overlapping communities. Technical report (2008)
11.
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)CrossRef
12.
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)CrossRef
13.
Rajaraman, A., Ullman, J.D., Ullman, J.D., Ullman, J.D.: Mining of Massive Datasets, vol. 1. Cambridge University Press, Cambridge (2012)MATH
14.
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)CrossRef
15.
Tang, L., Liu, H., Zhang, J., Nazeri, Z.: Community evolution in dynamic multi-mode networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 677–685. ACM (2008)
16.
Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. 45(4), 43 (2013)CrossRefMATH
17.
Xie, J., Szymanski, B.K., Liu, X.: SLPA: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: Proceedings of the 11th International Conference on Data Mining Workshops (ICDMW), pp. 344–349. IEEE (2011)
Metadata
Title
A Centrality-Based Local-First Approach for Analyzing Overlapping Communities in Dynamic Networks
Authors
Ximan Chen
Heli Sun
Hongxia Du
Jianbin Huang
Ke Liu
Copyright Year
2017
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-57528-5

Electronic ISBN: 978-3-319-57529-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-57529-2_40

Premium Partner

    Image Credits