Community Detection Using Maximum Connection Probability in Opportunistic Network

A novel approach is proposed in this paper to detect community structure in opportunistic networks. Different from the existing solutions, this approach uses Maximum Connection Probability (MCP) instead of encounter probability. This approach is established in two phases. Firstly, an algorithm is proposed to derive the MCP of any node to other nodes. Secondly, the community structure derived from the MCP is identified using a divisive algorithm. Simulation is conducted based on walking day movement model to evaluate the approach. The results show that the proposed approach can detect community structure more accurately and reflect human relationship in reality.

[1]  Marco Conti,et al.  Autonomic detection of dynamic social communities in Opportunistic Networks , 2011, 2011 The 10th IFIP Annual Mediterranean Ad Hoc Networking Workshop.

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

[3]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[4]  Jörg Ott,et al.  The ONE simulator for DTN protocol evaluation , 2009, SIMUTools 2009.

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

[6]  Marco Conti,et al.  HiBOp: a History Based Routing Protocol for Opportunistic Networks , 2007, 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[7]  V. Stavroulaki,et al.  Opportunistic Networks , 2011, IEEE Vehicular Technology Magazine.

[8]  Marco Conti,et al.  Human mobility models for opportunistic networks , 2011, IEEE Communications Magazine.

[9]  Kuang Xu,et al.  Detecting dynamic communities in opportunistic networks , 2009, 2009 First International Conference on Ubiquitous and Future Networks.

[10]  Marco Conti,et al.  Opportunistic networking: data forwarding in disconnected mobile ad hoc networks , 2006, IEEE Communications Magazine.

[11]  Jean-Yves Le Boudec,et al.  Power Law and Exponential Decay of Intercontact Times between Mobile Devices , 2007, IEEE Transactions on Mobile Computing.

[12]  Jörg Ott,et al.  Working day movement model , 2008, MobilityModels '08.

[13]  Pan Hui,et al.  Distributed community detection in delay tolerant networks , 2007, MobiArch '07.

[14]  Yong Zhang,et al.  Heterogeneous context-aware routing protocol for opportunistic network , 2010, 5th International Conference on Pervasive Computing and Applications.

[15]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[16]  Leandros Tassiulas,et al.  Social network analysis concepts in the design of wireless Ad Hoc network protocols , 2010, IEEE Network.

[17]  Cecilia Mascolo,et al.  A community based mobility model for ad hoc network research , 2006, REALMAN '06.

[18]  Christophe Diot,et al.  Impact of Human Mobility on Opportunistic Forwarding Algorithms , 2007, IEEE Transactions on Mobile Computing.

[19]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[20]  Antoine Dutot,et al.  Detecting Community Structure in Amino Acid Interaction Networks , 2009 .

[21]  Jian Ma,et al.  A Data Transmission Scheme for Community-Based Opportunistic Networks , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[22]  Pan Hui,et al.  BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks , 2008, IEEE Transactions on Mobile Computing.