ITM Web Conf.
Volume 7, 20163rd Annual International Conference on Information Technology and Applications (ITA 2016)
|Number of page(s)||6|
|Section||Session 5: Algorithms and Simulation|
|Published online||21 November 2016|
- Huang, C. M., Lan, K. C., & Tsai, C. Z. A survey of opportunistic networks. In Advanced Information Networking and Applications-Workshops. 22nd International Conference on IEEE (pp. 1672–1677)(2008). [Google Scholar]
- Daly, E. M., & Haahr, M. Social network analysis for routing in disconnected delay-tolerant manets. In Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing (pp. 32–40) (2007). [Google Scholar]
- Hossmann, T., Spyropoulos, T., & Legendre, F. Know thy neighbor: Towards optimal mapping of contacts to social graphs for dtn routing. In INFOCOM, 2010 Proceedings IEEE (pp. 1–9)(2010). [Google Scholar]
- Hui, P., Crowcroft, J., & Yoneki, E. Bubble rap: Social-based forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing, 10(11), 1576–1589(2011). [CrossRef] [Google Scholar]
- Gao, W., Li, Q., Zhao, B., & Cao, G. Social-aware multicast in disruption-tolerant networks. IEEE/ACM Transactions on Networking (TON), 20(5), 1553–1566(2012). [CrossRef] [Google Scholar]
- Gao, W., et al., On Exploiting Transient Social Contact Patterns for Data Forwarding in Delay-Tolerant Networks. IEEE Transactions on Mobile Computing, 12(1): p. 151–165(2013). [CrossRef] [Google Scholar]
- Becker, V. D. Epidemic routing for partially connected ad hoc networks. Proceedings of Technique Report, Department of Computer Science, Duke University, Durham, UK (2000). [Google Scholar]
- Yuan, Q., Cardei, I., & Wu, J. Predict and relay: an efficient routing in disruption-tolerant networks. In Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing (pp. 95–104)(2009). [CrossRef] [Google Scholar]
- Girvan, M. and Newman, M., Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12): p. 7821–7826(2002). [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Newman, M.E. and Girvan, M., Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys, 69(2 Pt 2): p. 026113(2004). [CrossRef] [MathSciNet] [Google Scholar]
- Palla, G., et al., Uncovering the overlapping community structure of complex networks in nature and society. NATURE, 435(7043): p. 814–818(2005). [CrossRef] [PubMed] [Google Scholar]
- Raghavan, U.N., Albert, R. and Kumara, S., Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E Stat Nonlin Soft Matter Phys, 76(3 Pt 2): p. 036106(2007). [CrossRef] [Google Scholar]
- Xie, J, S.B.K.L., SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process. (2011). [Google Scholar]
- Gregory, S., Finding overlapping communities in networks by label propagation. NEW JOURNAL OF PHYSICS, 12(103018) ( 2010). [CrossRef] [Google Scholar]
- Wu, Z., et al., Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks. Journal of Computer Science and Technology, 27(3): p. 468–479(2012). [CrossRef] [MathSciNet] [Google Scholar]
- Liu, Q., et al., An Interest Community Routing Scheme for Opportunistic Networks, in IEEE Global Telecommunications Conference (Globecom). p. 4366–4371(2013). [Google Scholar]
- Lu, Z, Sun, X, Wen, Y, et al. Algorithms and applications for community detection in weighted networks[J]. Parallel and Distributed Systems, IEEE Transactions on, 26(11): 2916–2926(2015). [CrossRef] [Google Scholar]
- Lancichinetti, A, Fortunato, S. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities[J]. Physical Review E, 80(1): 016118 (2009). [CrossRef] [Google Scholar]
- Ma, X. and Bai, X., A Community-Based Routing Algorithm for Opportunistic Networks, in International Conference on Ubiquitous and Future Networks. p. 701–706(2013). [Google Scholar]
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