Self-tuned kernel spectral clustering for large scale networks

RaghvenPhDa Mall, Rocco Langone, Johan A.K. Suykens

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

We propose a parameter-free kernel spectral clustering model for large scale complex networks. The kernel spectral clustering (KSC) method works by creating a model on a subgraph of the complex network. The model requires a kernel function which can have parameters and the number of communities k has be detected in the large scale network. We exploit the structure of the projections in the eigenspace to automatically identify the number of clusters. We use the concept of entropy and balanced clusters for this purpose. We show the effectiveness of the proposed approach by comparing the cluster memberships w.r.t. several large scale community detection techniques like Louvain, Infomap and Bigclam methods. We conducted experiments on several synthetic networks of varying size and mixing parameter along with large scale real world experiments to show the efficiency of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages385-393
Number of pages9
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: 6 Oct 20139 Oct 2013

Other

Other2013 IEEE International Conference on Big Data, Big Data 2013
CountryUnited States
CitySanta Clara, CA
Period6/10/139/10/13

    Fingerprint

Keywords

  • kernel spectral clustering
  • number of clusters
  • parameter-free spectral clustering

ASJC Scopus subject areas

  • Software

Cite this

Mall, R., Langone, R., & Suykens, J. A. K. (2013). Self-tuned kernel spectral clustering for large scale networks. In Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013 (pp. 385-393). [6691599] https://doi.org/10.1109/BigData.2013.6691599