Abraão Guimarães
ABSTRACT
Diffusion processes in complex dynamic networks can arise, for instance, on data search, data routing, and information spreading. Therefore, understanding how to speed up the diffusion process is an important topic in the study of complex dynamic networks. In this paper, we shed light on how centrality measures and node dynamics coupled with simple diffusion models can help on accelerating the cover time in dynamic networks. Using data from systems with different characteristics, we show that if dynamics is disregarded, network cover time is highly underestimated. Moreover, using centrality accelerates the diffusion process over a different set of complex dynamic networks when compared with the random walk approach. For the best case, in order to cover 80% of nodes, fast centrality-driven diffusion reaches an improvement of 60%, i.e. when next-hop nodes are selected by using centrality measures. Additionally, we also propose and present the first results on how link prediction can help on speeding up the diffusion process in dynamic networks.
- L. A. Adamic and E. Adar. How to search a social network. Social Networks, 27:187--203, 2005.Google Scholar
Cross Ref
- J. I. Alvarez-Hamelin, E. Fleury, A. Vespignani, and A. Ziviani. Complex dynamic networks: Tools and methods (Guest Editorial). Computer Networks, 56(3):967--969, Dec. 2012. Google ScholarDigital Library
- M. V. Barbera, J. Stefa, A. C. Viana, M. D. de Amorim, and M. Boc. VIP delegation: Enabling VIPs to offload data in wireless social mobile networks. In Proc. of the Int. Conf. on Distributed Computing in Sensor Systems (DCOSS), pages 1--8, Barcelona, Spain, June 2011. IEEE.Google ScholarCross Ref
- P. Basu, A. Bar-Noy, R. Ramanathan, and M. P. Johnson. Modeling and analysis of time-varying graphs. CoRR, abs/1012.0260, 2010.Google Scholar
- M. Boguñá, D. Krioukov, and k. claffy. Navigability of Complex Networks. Nature Physics, 5(1):74--80, 2009.Google ScholarCross Ref
- S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1):107--117, 1998. Google ScholarDigital Library
- O. Şimşek and D. Jensen. Navigating networks by using homophily and degree. Proceedings of the National Academy of Sciences, 105(35):12758--12762, Sept. 2008.Google ScholarCross Ref
- S. Dorogovtsev and J. Mendes. Evolution of Networks: From biological networks to the Internet and WWW. Oxford University Press, Inc., New York, NY, USA, 2003. Google ScholarDigital Library
- D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, 2010. Google ScholarCross Ref
- M. Everett and S. Borgatti. Ego network betweenness. Social Networks, 27(1):31--38, Jan. 2005.Google ScholarCross Ref
- J. Gómez-Gardeñes and V. Latora. Entropy rate of diffusion processes on complex networks. Physcal Review E, 78:065102, Dec 2008.Google ScholarCross Ref
- D. A. Guedes, E. S. Silva, A. Ziviani, and K. V. Cardoso. Dynamic labeling in wireless mesh networks. In Proc. of the IEEE Latin-American Conference on Communications (IEEE LATINCOM), Cuenca, Ecuador, 2012.Google ScholarCross Ref
- P. Holme and J. Saramäki. Temporal networks. Physics Reports, 519(3):97--125, Oct. 2012.Google ScholarCross Ref
- P. Hui, J. Crowcroft, and E. Yoneki. Bubble rap: social-based forwarding in delay tolerant networks. In Proc. of the ACM Int. Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pages 241--250, Hong Kong, China, 2008. Google ScholarDigital Library
- B. J. Kim, C. N. Yoon, S. K. Han, and H. Jeong. Path finding strategies in scale-free networks. Physical Review E, 65:027103, Jan 2002.Google ScholarCross Ref
- H. Kim and R. Anderson. Temporal node centrality in complex networks. Physical Review E, 85(2):026107, Feb. 2012.Google ScholarCross Ref
- M. Kitsak, L. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. Stanley, and H. Makse. Identification of influential spreaders in complex networks. Nature Physics, 6(11):888--893, Aug 2010.Google ScholarCross Ref
- L. Kocarev. Network Science: A New Paradigm Shift. IEEE Network, 24(6):6--9, 2010. Google ScholarDigital Library
- V. Kostakos. Temporal graphs. Physica A: Statistical Mechanics and its Applications, 388(6):1007--1023, Mar. 2009.Google ScholarCross Ref
- J. Leskovec, J. Kleinberg, and C. Faloutsos. Graphs over time. In Proc. of the ACM Int. Conference on Knowledge Discovery in Data Mining (KDD), pages 177--187, 2005. Google ScholarDigital Library
- L. Lovasz. Random Walks on Graphs: A Survey. Bolyai Society Mathematical Studies, 2:1--46, 1993.Google Scholar
- R. M. Lukose, L. A. Adamic, A. R. Puniyani, and B. A. Huberman. Search in power-law networks. Physical Review E, 64(4), 2001.Google Scholar
- A. Mtibaa, M. May, C. Diot, and M. Ammar. PeopleRank: Social opportunistic forwarding. In Proc. of the IEEE INFOCOM, pages 1--5, San Diego, CA, USA, Mar. 2010. IEEE. Google ScholarDigital Library
- M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45(2):167--256, 2003.Google ScholarDigital Library
- R. Pan and J. Saramäki. Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1):1--10, July 2011.Google ScholarCross Ref
- M. Rosvall, A. Gronlund, P. Minnhagen, and K. Sneppen. Searchability of networks. Physical Review E, 72(4), Oct 2005.Google ScholarCross Ref
- J. Scott, R. Gass, J. Crowcroft, P. Hui, C. Diot, and A. Chaintreau. CRAWDAD trace cambridge/haggle/imote/infocom2006 (v. 2009-05-29). Downloaded from http://crawdad.cs.dartmouth.edu/cambridge/haggle/imote/infocom2006, May 2009.Google Scholar
- J. Tang, M. Musolesi, C. Mascolo, V. Latora, and V. Nicosia. Analysing information flows and key mediators through temporal centrality metrics. In Proc. of the Workshop on Social Network Systems (SNS), pages 1--6, 2010. Google ScholarDigital Library
- T. W. Valente, K. Coronges, C. Lakon, and E. Costenbader. How correlated are network centrality measures? Connections (Toronto, Ont.), 28(1):16, 2008.Google Scholar
- A. B. Vieira, A. P. C. da Silva, F. H. Cerqueira, G. Goncalves, and P. Gomes. Sopcast p2p live streaming: Live session traces and analysis. In Proceedings of ACM Multimedia Systems Conference, March 2013. Google ScholarDigital Library
- K. Wehmuth and A. Ziviani. Distributed assessment of the closeness centrality ranking in complex networks. In 4th Workshop on Simplifying Complex Networks for Practitioners (SIMPLEX), WWW 2012, pages 43--48, 2012. Google ScholarDigital Library
- N. Yoshida. Estimation for diffusion processes from discrete observation. Journal of Multivariate Analysis, 41(2):220--242, 1992. Google ScholarDigital Library
- P. Yuan and H. Ma. Hug: Human gathering point based routing for opportunistic networks. In Proc. of the IEEE Wireless Communications and Networking Conference (WCNC), pages 3024--3029, Paris, France, Apr. 2012. IEEE.Google ScholarCross Ref
Index Terms
- Fast centrality-driven diffusion in dynamic networks
Recommendations
Centrality metric for dynamic networks
MLG '10: Proceedings of the Eighth Workshop on Mining and Learning with GraphsCentrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static ...
Fast incremental computation of harmonic closeness centrality in directed weighted networks
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningThis paper proposes a novel approach to efficiently compute the exact closeness centrality values of all nodes in dynamically evolving directed and weighted networks. Closeness centrality is one of the most frequently used centrality measures in the ...
Centrality, gossip, and diffusion of information in networks
EC '14: Proceedings of the fifteenth ACM conference on Economics and computationHow can we identify the most influential nodes in a network for initiating diffusion? Are people able to easily identify those people in their communities who are best at spreading information, and if so How? Using theory and recent data, we will ...
Comments