Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-26T07:59:42.136Z Has data issue: false hasContentIssue false
  • English
  • Français

Brokerage-based attack on real world temporal networks

Published online by Cambridge University Press:  07 November 2016

SOUVIK SUR ,
NILOY GANGULY  and
ANIMESH MUKHERJEE
SOUVIK SUR
Affiliation:
School of Telecommunications, Indian Institute of Technology, Kharagpur, 721302, India (e-mail: [email protected])
NILOY GANGULY
Affiliation:
Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, 721302, India (e-mail: [email protected], [email protected])
ANIMESH MUKHERJEE
Affiliation:
Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, 721302, India (e-mail: [email protected], [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, we attempt to investigate the attack tolerance of human mobility networks where the mobility is restricted to some extent, for instance, in a hospital, one is not allowed to access all locations. Similar situations also arise in schools. In such a network, we will show that people need to rely upon some intermediate agents, popularly known as the brokers to disseminate information. In order to establish this fact, we have followed the approach of attack in a network which in turn helps to identify important nodes in the network in order to maintain the overall connectivity. In this direction, we have proposed, a new temporal metric, brokerage frequency which significantly outperforms all other state-of-the-art attack strategies reported in Trajanovski et al. (2012), Sur et al. (2015).

Keywords

temporal networksattack tolerancetemporal brokerage
Type
Research Article
Information
Network Science , Volume 4 , Issue 4 , December 2016 , pp. 446 - 459
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albert, R., Jeong, H., & Barabasi, A. L. (2000). Error and attack tolerance of complex networks. Nature, 406 (6794), 378–382.Google Scholar
Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92 (5), 1170–1182.Google Scholar
Burt, R. S. (1976). Positions in network. Social Forces, 55 (1), 93–122.CrossRefGoogle Scholar
Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70 (Dec), 066111.Google Scholar
Cook, K. S., Emerson, R. M., Gillmore, M. R., & Yamagishi, T. (1983). The distribution of power in exchange networks: Theory and experimental results. American Journal of Sociology, 89 (2), pp. 275–305.Google Scholar
Cook, K. S. (1981). Network structures from an exchange perspective. State University of New York, Department of Sociology.Google Scholar
Daly, E. M., & Haahr, M. (2007). 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. MobiHoc '07 (pp. 32–40). New York, NY, USA: ACM.Google Scholar
Fournet, J., & Barrat, A. (2014). Contact patterns among high school students. Plos One, 9 (9), e107878.CrossRefGoogle ScholarPubMed
Funke, S., Guibas, L. J., Nguyen, A., & Wang, Y. (2006). Distributed Computing in Sensor Systems: Second IEEE International Conference, DCOSS 2006, san francisco, ca, usa, june 18–20, 2006. Proceedings (pp. 234–251). Berlin, Heidelberg: Springer. Chap. Distance-Sensitive Information Brokerage in Sensor Networks.Google Scholar
Gould, R. V., & Fernandez, R. M. (1989). Structures of mediation: A formal approach to Brokerage in Transaction Networks. Sociological Methodology, 19, 89–126.Google Scholar
Grabowicz, P. A., Ramasco, J. J., Moro, E., Pujol, J. M., & Eguiluz, V. M. (2012). Social features of online networks: The strength of intermediary ties in online social media. Plos One, 7 (1), e29358.CrossRefGoogle ScholarPubMed
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78 (6), 1360–1380.Google Scholar
Hanneman, R. A., & Riddle, M. (2005). Introduction to Social Network Methods. Retrieved from http://faculty.ucr.edu/~hanneman/nettext/C9_Ego_networks.html#brokerage. [Online; accessed 13 December 2016].Google Scholar
Heemskerk, E. M., Daolio, F., & Tomassini, M. (2013). The community structure of the european network of interlocking directorates 2005–2010. Plos One, 8 (7), e68581.CrossRefGoogle ScholarPubMed
Holme, P., Kim, B. J., Yoon, C. N., & Han, S. K. (2002). Attack vulnerability of complex networks. Physical Review E, 65 (May), 056109.Google Scholar
Hui, P., Crowcroft, J., & Yoneki, E. (2011). Bubble rap: Social-based forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing, 10 (11), 1576–1589.Google Scholar
Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.-F., & Van den Broeck, W. (2011). What's in a crowd? Analysis of face-to-face behavioral networks. Journal of Theoretical Biology, 271 (1), 166–180.Google Scholar
Jiao, Q.-J., Huang, Y., Liu, W., Wang, X.-F., Chen, X.-S., & Shen, H.-B. (2013). Revealing the hidden relationship by sparse modules in complex networks with a large-scale analysis. Plos One, 8 (6), e66020.Google Scholar
Latora, V., & Marchiori, M. (2005). Vulnerability and protection of infrastructure networks. Physical Review E, 71 (Jan), 015103.Google Scholar
Lin, C.-H., Kuo, J.-J., & Tsai, M.-J. (2010). Reliable GPS-free double-ruling-based information brokerage in wireless sensor networks. In: Proceedings of the 29th conference on information communications (pp. 351–355). INFOCOM'10. Piscataway, NJ, USA: IEEE Press.Google Scholar
Lind, B. E., Tirado, M., Butts, C. T. & Petrescu-Prahova, M. (2008). Brokerage roles in disaster response: Organisational mediation in the wake of Hurricane Katrina. International Journal of Emergency Management, 5 (1–2), 75–99.Google Scholar
Marsden, P. V. (1982). Brokerage behavior in restricted exchange networks. In Marsden, P. V., & Lin, N. (Eds.), Social structure and network analysis (pp. 201–218). Beverly Hills, CA: Sage.Google Scholar
Newman, M. E. J., & Ghoshal, G. (2008). Bicomponents and the robustness of networks to failure. Physical Review Letters, 100 (Mar), 138701.Google Scholar
Pietilainen, A.-K. 2012 (July). CRAWDAD Data Set thlab/sigcomm2009 (v. 2012-07-15). Retrieved from http://crawdad.org/thlab/sigcomm2009/.Google Scholar
Sarkar, R., Zhu, X., & Gao, J. (2006). Double rulings for information brokerage in sensor networks. IEEE/ACM Transactions on Networking, 17, 286–297.Google Scholar
Scellato, S., Leontiadis, I, Mascolo, C., Basu, P., & Zafer, M. (2011 April). Understanding robustness of mobile networks through temporal network measures. In Proceedings of INFOCOM (pp. 1–5). Shanghai, China: IEEE.Google Scholar
Scott, J., Gass, R., Crowcroft, J., Hui, P., Diot, C., & Chaintreau, A. (2006 Jan.). CRAWDAD trace cambridge/haggle/imote/infocom (v. 2006-01-31). Downloaded from http://crawdad.cs.dartmouth.edu/cambridge/haggle/imote/infocom.Google Scholar
Scott, J., Gass, R., Crowcroft, J., Hui, P., Diot, C., & Chaintreau, A. (2009 May). CRAWDAD trace cambridge/haggle/imote/infocom2006 (v. 2009-05-29). Retrieved from http://crawdad.cs.dartmouth.edu/cambridge/haggle/imote/infocom2006.Google Scholar
Shah, R. C., Roy, S., Jain, S., & Brunette, W. (2003 May). Data mules: Modeling and analysis of a three-tier architecture for sparse sensor networks. Ad hoc networks, 1 (2), 215–233.Google Scholar
Sur, S., Ganguly, N., & Mukherjee, A. (2015). Attack tolerance of correlated time-varying social networks with well-defined communities. Physica A: Statistical Mechanics and its Applications, 420 (0), 98–107.Google Scholar
Trajanovski, S., Scellato, S., & Leontiadis, I. (2012). Error and attack vulnerability of temporal networks. Physical Review E, 85 (Jun), 066105.Google Scholar
Vanhems, P., Barrat, A., Cattuto, C., Pinton, J.-F., Khanafer, N., Régis, C., Kim, , . . . Voirin, N. (2013). Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. Plos One, 8 (9), e73970.Google Scholar
Weimann, G. (1982). On the importance of marginality: One more step into the two-step flow of communication. American Sociological Review, 47 (6), 764–773.Google Scholar
Yang, J., & Leskovec, J. (2012). Defining and evaluating network communities based on ground-truth. In Proceedings of the ACM sigkdd Workshop on Mining Data Semantics. MDS '12 (pp. 3:1–3:8). New York, NY, USA: ACM.Google Scholar
Zhao, W., Ammar, M., & Zegura, E. (2004). A message ferrying approach for data delivery in sparse mobile ad hoc networks. In Proceedings of the 5th ACM International Symposium on Mobile ad hoc Networking and Computing. MobiHoc '04 (pp. 187–198). New York, NY, USA: ACM.Google Scholar