A Novel Algorithm for Detecting Social Clusters and Hierarchical Structure in Industrial IoT

The rapid development of IoT has brought life around us with tremendous impact. Especially, industrial IoT as a new research hotspot, has been attracting extensive concern from industry and academia, facilitating many technologies and application in industrial IoT. However, taking full advantage of a large number of resources in industrial IoT is a challenging task. In this article, we present an efficient mobile social cluster algorithm (OMSC) to detect the potential social relationships among mobile devices in industrial IoT. It can discover the overlapping cluster and hierarchical structure in near-line time. We implement this algorithm in the Java Platform and validate the OMSC in synthetic networks and real-world network datasets. The experimental results demonstrate that the presented OMSC algorithm has high performance.

[1]  Dit-Yan Yeung,et al.  Overlapping community detection via bounded nonnegative matrix tri-factorization , 2012, KDD.

[2]  Jiawei Han,et al.  Density-based shrinkage for revealing hierarchical and overlapping community structure in networks , 2011 .

[3]  Jiafu Wan,et al.  Implementing Smart Factory of Industrie 4.0: An Outlook , 2016, Int. J. Distributed Sens. Networks.

[4]  L. Bécu,et al.  Evidence for three-dimensional unstable flows in shear-banding wormlike micelles. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Ali Aïtelhadj,et al.  Dual modularity optimization for detecting overlapping communities in bipartite networks , 2013, Knowledge and Information Systems.

[6]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

[7]  Maozhen Li,et al.  Cognitive Data Routing in Heterogeneous Mobile Cloud Networks , 2014, 2014 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering.

[8]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[9]  M. Markus,et al.  Fluctuation theorem for a deterministic one-particle system. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Jure Leskovec,et al.  Overlapping community detection at scale: a nonnegative matrix factorization approach , 2013, WSDM.

[11]  Bongkeun Kim,et al.  Calculations of the second virial coefficients of protein solutions with an extended fast multipole method. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  F. Radicchi,et al.  Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[14]  Jacek M. Zurada,et al.  Normalized Mutual Information Feature Selection , 2009, IEEE Transactions on Neural Networks.

[15]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Xu Chen,et al.  A social group utility maximization framework with applications in database assisted spectrum access , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[17]  Jure Leskovec,et al.  Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters , 2008, Internet Math..

[18]  Zhiwei Zhang,et al.  Mining overlapping and hierarchical communities in complex networks , 2015 .

[19]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  M. Newman Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Gregor E. Morfill,et al.  Effect of polarization force on the propagation of dust acoustic solitary waves , 2010 .

[22]  Lin Gao,et al.  Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks , 2011, Inf. Sci..

[23]  Min Chen,et al.  Software-defined internet of things for smart urban sensing , 2015, IEEE Communications Magazine.

[24]  Steve Gregory,et al.  Finding overlapping communities in networks by label propagation , 2009, ArXiv.

[25]  Stephen Roberts,et al.  Overlapping community detection using Bayesian non-negative matrix factorization. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.