Total views : 128

Sink Node Elimination to Enhance the Performance of Overlapping Detection Algorithms along with Comparison of Existing Algorithm


  • Department of C.S.E., IKG Punjab Technical University, Ibban, Kapurthala – 144603, Punjab, India


Objectives: To eliminate Sink nodes so that rate of detection can improve within the community overlapping detection and this also increases the modularity. It also consumes less time. Methods/Statistical Analysis: Modified k-clique Algorithm is used. Clique algorithm considers Sink nodes. Sink nodes are those which do not have any connecting edge. The proposed algorithm (MKC) does eliminate these nodes and hence consider only those nodes which are connected in nature. To detect the Sink nodes adjacency matrix is used. MATLAB is used for the simulation. Community detection toolbox is used which provides several functions for graph generations, clustering algorithms etc., Findings: This approach produces better result in terms of community finding. More community are discovered with the proposed approach and also entropy is improved greatly. Result in terms of time consumption is reduced almost by 50%. Application/Improvements: The length and complexity of the cliques found is reduced considerably. The speed is almost enhanced by 5% which can further be increased by using hop count mechanism in addition to the already used Sink node elimination.


Community Overlapping, k-clique, Sink Nodes.

Full Text:

 |  (PDF views: 79)


  • Ghosh S, Dubey S. Comparative analysis of K-means and Fuzzy C-Means Algorithms. IJACSA; 2013. p. 35–9.
  • Hung WL, Yang MS, Chen DH. Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation. Pattern Recognit Lett. 2006; 27(5):424–38. Crossref
  • Chatzis SP. A fuzzy c-means-type algorithms for clumping of data with mixed numeric and categorical attributes employing a probabilistic dissimilarity functional. Expert Syst Appl. 2011; 38(7):8684–9. Crossref
  • Ji Z, Xia Y, Sun Q, Cao G. Interval-valued possibilistic fuzzy C-means clumping algorithm. Fuzzy Sets Syst. 2014; 253:138–56. Crossref
  • Mingoti SA, Lima JO. Comparing SOM neural network with Fuzzy C-means, K-means and traditional hierarchical clumping algorithms. Eur J Oper Res. 2006; 174:1742–59. Crossref
  • Wang W, Zhang Y. On fuzzy clump validity indices. Fuzzy Sets Syst. 2007; 158(19):2095–117. Crossref
  • Wang X, Wang Y, Wang L. Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognit Lett. 2004; 25(10):1123–32.
  • Wu KL. Analysis of parameter selections for fuzzy c-means. Pattern Recognitt. 2012; 45(1):407–15. Crossref
  • Wu KL,Yang MS. Alternative c-means clustering algorithms. Pattern Recognitt. 2002; 35(10):2267–78. Crossref
  • Zhu W, Jiang J, Song C, Bao L. Clustering algorithm based on Fuzzy C-means and Artificial Fish Swarm. Procedia Eng. 2012; 29:3307–11. Crossref
  • Jierui X, Stephen K, Boleslaw KS. Overlapping Group Detection in Networks: The State-of-the-Art and Comparative Study. ACM Computing Surveys. 2013 Aug; 45(4):43.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.