Jian Dong
ABSTRACT
Community detection is an important way to understand the functions and characteristics of complex systems. The Label Propagation Algorithm (LPA) is a community detection algorithm of which complexity is close to linear. Due to the randomness and instability of the algorithm, it is difficult to get good results with this algorithm. This paper proposes a degree-based label propagation algorithm and parallelizes the algorithm based on the GraphX component in Spark platform (D-disLPA). Using WeChat empirical network data, this paper adopts modularity as an evaluation index to compare D-disLPA with traditional label propagation algorithms. The experimental results show that the D-disLPA can effectively solve the non-convergence problem in traditional algorithms and improve the stability and accuracy of community detection. At the same time, this distributed algorithm is able to satisfy the requirements of large-scale community detection.
- JR Banavar, A Maritan, and A Rinaldo. 1999. Size and form in efficient transportation networks. Nature 399, 6732 (May 1999), 130âĂŤ132.Google Scholar
Cross Ref
- M.J. Barber and J. W. Clark. 2009. Detecting network communities by propagating labels under constraints. pre 80, 2 (aug 2009), 026129.Google Scholar
- Anthony Chen, Hai Yang, Hong K. Lo, and Wilson H. Tang. {n. d.}. A capacity related reliability for transportation networks. Journal of Advanced Transportation 33, 2 ({n. d.}), 183--200. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/atr.5670330207Google Scholar
- A. Chuan, B. Chen, L. Liu, J. Dong, L. Hey, and X. Qiu. 2018. Design and Implementation of Information Dissemination Simulation Algorithm in Large-Scale Complex Network Based on Spark. In 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), Vol. 00. 457--464.Google Scholar
- G. Cordasco and L. Gargano. 2011. Community Detection via Semi-Synchronous Label Propagation Algorithms. ArXiv e-prints (March 2011). arXiv:1103.4550Google Scholar
- Jeffrey Dean and Sanjay Ghemawat. 2004. MapReduce: Simplified Data Processing on Large Clusters. In Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation - Volume 6 (OSDI'04). USENIX Association, Berkeley, CA, USA, 10--10. http://dl.acm.org/citation.cfm?id=1251254.1251264 Google ScholarDigital Library
- Santo Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3 (2010), 75--174.Google ScholarCross Ref
- Linton C. Freeman. 1978. Centrality in social networks conceptual clarification. Social Networks 1, 3 (1978), 215--239.Google ScholarCross Ref
- Alexandros V. Gerbessiotis and Leslie G. Valiant. 1992. Direct Bulk-Synchronous Parallel Algorithms. In Proceedings of the Third Scandinavian Workshop on Algorithm Theory (SWAT '92). Springer-Verlag, London, UK, UK, 1--18. http://dl.acm.org/citation.cfm?id=645896.671952 Google ScholarDigital Library
- M. Girvan and M. E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 12 (2002), 7821--7826. arXiv:http://www.pnas.org/content/99/12/7821.full.pdfGoogle ScholarCross Ref
- D. E. GOLDBERG. 1989. Genetic Algorithm in Search Optimization and Machine Learning. Addison Wesley (1989). https://ci.nii.ac.jp/naid/10006087555/en/ Google ScholarDigital Library
- Wei Liu, Matteo Pellegrini, and Xiaofan Wang. 2014. Detecting communities based on network topology. Scientific reports 4 (2014), 5739.Google Scholar
- Grzegorz Malewicz, Matthew H. Austern, Aart J.C Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: A System for Large-scale Graph Processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD '10). ACM, New York, NY, USA, 135--146. Google ScholarDigital Library
- Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. 2007. Measurement and Analysis of Online Social Networks. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC '07). ACM, New York, NY, USA, 29--42. Google ScholarDigital Library
- ME Newman. 2004. Fast algorithm for detecting community structure in networks. Physical review. E, Statistical, nonlinear, and soft matter physics 69, 6 Pt 2 (June 2004), 066133.Google Scholar
- ME Newman and M Girvan. 2004. Finding and evaluating community structure in networks. Physical review. E, Statistical, nonlinear, and soft matter physics 69, 2 Pt 2 (February 2004), 026113.Google Scholar
- G. Palla, I. Derényi, I. Farkas, and T. Vicsek. 2005. Uncovering the overlapping community structure of complex networks in nature and society. nat 435 (2005), 814--818.Google Scholar
- Alex Pothen, Horst D. Simon, and Kan-Pu Liou. 1990. Partitioning Sparse Matrices with Eigenvectors of Graphs. SIAM J. Matrix Anal. Appl. 11, 3 (May 1990), 430--452. Google ScholarDigital Library
- Usha Nandini Raghavan, RÃl'ka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical review. E, Statistical, nonlinear, and soft matter physics 76, 3 Pt 2 (2007), 036106.Google Scholar
- Eric E Schadt. 2009. Molecular networks as sensors and drivers of common human diseases. Nature 461, 7261 (September 2009), 218âĂŤ223.Google ScholarCross Ref
- Philipp Schuetz and Amedeo Caflisch. 2008. Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Physical review. E, Statistical, nonlinear, and soft matter physics 77, 4 Pt 2 (April 2008), 046112.Google Scholar
- Vinod Kumar Vavilapalli, Arun C. Murthy, Chris Douglas, Sharad Agarwal, Mahadev Konar, Robert Evans, Thomas Graves, Jason Lowe, Hitesh Shah, Siddharth Seth, Bikas Saha, Carlo Curino, Owen O'Malley, Sanjay Radia, Benjamin Reed, and Eric Baldeschwieler. 2013. Apache Hadoop YARN: Yet Another Resource Negotiator. In Proceedings of the 4th Annual Symposium on Cloud Computing (SOCC '13). ACM, New York, NY, USA, Article 5, 16 pages. Google ScholarDigital Library
- Jierui Xie and Boleslaw K. Szymanski. 2011. Community Detection Using a Neighborhood Strength Driven Label Propagation Algorithm. In Proceedings of the 2011 IEEE Network Science Workshop (NSW '11). IEEE Computer Society, Washington, DC, USA, 188--195. Google ScholarDigital Library
- Reynold S. Xin, Joseph E. Gonzalez, Michael J. Franklin, and Ion Stoica. 2013. GraphX: A Resilient Distributed Graph System on Spark. In First International Workshop on Graph Data Management Experiences and Systems (GRADES '13). ACM, New York, NY, USA, Article 2, 6 pages. Google ScholarDigital Library
- Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster Computing with Working Sets. In Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud'10). USENIX Association, Berkeley, CA, USA, 10--10. http://dl.acm.org/citation.cfm?id=1863103.1863113 Google ScholarDigital Library
Index Terms
- A Degree-based Distributed Label Propagation Algorithm for Community Detection in Networks
Recommendations
Signed Network Label Propagation Algorithm with Structural Balance Degree for Community Detection
ICOST 2016: Proceedings of the 14th International Conference on Inclusive Smart Cities and Digital Health - Volume 9677Social networks are usually modeled as signed networks. The community detection is an important problem for the research of signed networks. The time complexity of signed label propagation algorithm is lower than most existing algorithms for community ...
Community detection using constrained label propagation algorithm with nodes exemption
AbstractThe detection of community structure plays an important role in understanding the properties and characteristics of complex networks. The label propagation algorithm (LPA) emerges as a popular community detection method, due to its simplicity and ...
Community detection using a neighborhood strength driven Label Propagation Algorithm
NSW '11: Proceedings of the 2011 IEEE Network Science WorkshopStudies of community structure and evolution in large social networks require a fast and accurate algorithm for community detection. As the size of analyzed communities grows, complexity of the community detection algorithm needs to be kept close to ...
Comments