Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-24T04:29:47.660Z Has data issue: false hasContentIssue false

11 - Social influence analysis in the big data era: a review

from Part III - Big data over social networks

Published online by Cambridge University Press:  18 December 2015

By
Jianping Cao ,
Dongliang Duan ,
Liuqing Yang ,
Qingpeng Zhang ,
Senzhang Wang  and
Feiyue Wang
Edited by
Shuguang Cui ,
Alfred O. Hero, III ,
Zhi-Quan Luo  and
José M. F. Moura
Jianping Cao
Affiliation:
National University of Defense Technology, China
Dongliang Duan
Affiliation:
University of Wyoming, USA
Liuqing Yang
Affiliation:
Colorado State University, USA
Qingpeng Zhang
Affiliation:
City University of Hong Kong, China
Senzhang Wang
Affiliation:
Beihang Univerisity, China
Feiyue Wang
Affiliation:
National University of Defense Technology, China
Shuguang Cui
Affiliation:
Texas A & M University
Alfred O. Hero, III
Affiliation:
University of Michigan, Ann Arbor
Zhi-Quan Luo
Affiliation:
University of Minnesota
José M. F. Moura
Affiliation:
Carnegie Mellon University, Pennsylvania
Get access

Summary

Social influence is a widely accepted phenomenon in social networks, and it has been studied by researchers from various perspectives, including social psychology, sociology, marketing, and computer science, just to name a few. During the past decade, the emergence and fast growth of social media sites (such as Facebook and Twitter) have enabled the measurement, quantitative analysis, and modeling of social influence at a large scale. Therefore, it is essential to re-evaluate these developed algorithms and models in the new era of big data. In this chapter, we review research on social influence analysis in the big data era, with a focus on the computational perspective.We first present the statistical measurements of social influence. Then, we introduce the algorithms and models to characterize the propagation of social influence. Next, we present the issues related to the optimization of the propagation of social influence. In addition, we review research on the diffusion of network influence, which is closely related to the studies of the forecasting and influencing/contagion of information. Towards the end of this chapter, we also discuss the envisioned opportunities and challenges.

Introduction

Social influence analysis is an intuitive and well-accepted phenomenon by researchers for decades [1, 2]. Since social influence plays a key role in social life and decision making, as discovered by Katz and Lazarsf in the 1950s [3], theories and models have been developed from various perspectives by researchers in many different areas, including sociology, computer science, and management science, etc. With the popularity of social network services, increasing computer science researchers are paying more attention to this field. Social influence has extensive qualitative and quantitative applications, which have been well studied in sociology and computer science. For example, public opinion leaders affect numerous fans, and their opinions are quickly spread to a large population. Since they play an essential role in information dissemination, many studies focused on the identification of those users [4–6]. Social influence analysis has also been applied to other fields, such as recommendation systems [7], information propagation in social networks [1, 8–11], link prediction [12–14], viral marketing [15–21], public health [22, 23], expert discovery [24, 25], detection of emergent events [26], and advertising [27], just to name a few. In this chapter, we focus on the “social influence analysis” based on social networks such as Twitter, Facebook, and Weibo.

Type
Chapter
Information
Big Data over Networks , pp. 301 - 334
Publisher: Cambridge University Press
Print publication year: 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

[1] D.M., Romero, W., Galuba, S., Asur, and B. A., Huberman, “Influence and passivity in social media,” in Machine Learning and Knowledge Discovery in Databases, Springer, 2011, pp. 18–33.Google Scholar
[2] D., Easley and J., Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Cambridge University Press, 2010.Google Scholar
[3] L., Katz, “A new status index derived from sociometric analysis,” Psychometrika, vol. 18, no. 1, pp. 39–43, 1953.Google Scholar
[4] E., Katz and P. F., Lazarsfeld, Personal Influence, The Part Played by People in the Flow of Mass Communications, Transaction Publishers, 1970.Google Scholar
[5] E. M., Rogers, Diffusion of Innovations, Simon and Schuster, 2010.Google Scholar
[6] E., Keller and J., Berry, The Influentials: One American in Ten Tells the Other Nine How to Vote, Where to Eat, and What to Buy, Simon and Schuster, 2003.Google Scholar
[7] G. K., AlMamunur Rashid and J., Riedl, “Influence in ratings-based recommender systems: an algorithm-independent approach,” in Proceedings of the SIAM International Conference on Data Mining, SIAM, 2005.Google Scholar
[8] E., Bakshy, J. M., Hofman, W. A., Mason, and D. J., Watts, “Everyone's an influencer: quantifying influence on twitter,” in Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, ACM, 2011, pp. 65–74.Google Scholar
[9] M., Cha, H., Haddadi, F., Benevenuto, and P. K., Gummadi, “Measuring user influence in twitter: the million follower fallacy,” ICWSM, vol. 10, pp. 10–17, 2010.Google Scholar
[10] J., Weng, E.-P., Lim, J., Jiang, and Q., He, “Twitterrank: finding topic-sensitive influential twitterers,” in Proceedings of the Third ACM International Conference on Web Search and Data Mining, ACM, 2010, pp. 261–270.Google Scholar
[11] J., Yang and J., Leskovec, “Modeling information diffusion in implicit networks,” in Data Mining (ICDM), 2010 IEEE 10th International Conference on, IEEE, 2010, pp. 599–608.Google Scholar
[12] L., Backstrom, D., Huttenlocher, J., Kleinberg, and X., Lan, “Group formation in large social networks: membership, growth, and evolution,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2006, pp. 44–54.Google Scholar
[13] D., Crandall, D., Cosley, D., Huttenlocher, J., Kleinberg, and S., Suri, “Feedback effects between similarity and social influence in online communities,” in Proceedings of the 14th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2008, pp. 160–168.Google Scholar
[14] L. M., Aiello, A., Barrat, R., Schifanella, et al., “Friendship prediction and homophily in social media,” ACM Transactions on the Web (TWEB), vol. 6, no. 2, p. 9, 2012.Google Scholar
[15] D., Kempe, J., Kleinberg, and É., Tardos, “Influential nodes in a diffusion model for social networks,” in Automata, Languages and Programming, Springer, 2005, pp. 1127–1138.Google Scholar
[16] J. J., Brown and P. H., Reingen, “Social ties and word-of-mouth referral behavior,” Journal of Consumer Research, pp. 350–362, 1987.Google Scholar
[17] V., Mahajan, E., Muller, and F. M., Bass, “New product diffusion models in marketing: A review and directions for research,” The Journal of Marketing, pp. 1–26, 1990.Google Scholar
[18] P., Domingos and M., Richardson, “Mining the network value of customers,” in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2001, pp. 57–66.Google Scholar
[19] J., Goldenberg, B., Libai, and E., Muller, “Talk of the network: a complex systems look at the underlying process of word-of-mouth,” Marketing Letters, vol. 12, no. 3, pp. 211–223, 2001.Google Scholar
[20] M., Richardson and P., Domingos, “Mining knowledge-sharing sites for viral marketing,” in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2002, pp. 61–70.Google Scholar
[21] J., Leskovec, L. A., Adamic, and B. A., Huberman, “The dynamics of viral marketing,” ACM Transactions on the Web (TWEB), vol. 1, no. 1, p. 5, 2007.Google Scholar
[22] N. A., Christakis and J. H., Fowler, “The spread of obesity in a large social network over 32 years,” New England Journal of Medicine, vol. 357, no. 4, pp. 370–379, 2007.Google Scholar
[23] J. H., Fowler and N. A., Christakis, “Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the framingham heart study,” BMJ, vol. 337, 2008.Google Scholar
[24] W., Dong and A., Pentland, “Modeling influence between experts,” in Artifical Intelligence for Human Computing, Springer, 2007, pp. 170–189.Google Scholar
[25] J., Tang, J., Sun, C., Wang, and Z., Yang, “Social influence analysis in large-scale networks,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2009, pp. 807–816.Google Scholar
[26] T., Sakaki, M., Okazaki, and Y., Matsuo, “Earthquake shakes twitter users: real-time event detection by social sensors,” in Proceedings of the 19th International Conference on World Wide Web, ACM, 2010, pp. 851–860.Google Scholar
[27] E., Bakshy, D., Eckles, R., Yan, and I., Rosenn, “Social influence in social advertising: evidence from field experiments,” in Proceedings of the 13th ACMConference on Electronic Commerce, ACM, 2012, pp. 146–161.Google Scholar
[28] L., Rashotte, “Social influence,” The Blackwell Encyclopedia of Social Psychology, vol. 9, pp. 562–563, 2007.Google Scholar
[29] D. J., Watts and P. S., Dodds, “Influentials, networks, and public opinion formation,” Journal of Consumer Research, vol. 34, no. 4, pp. 441–458, 2007.Google Scholar
[30] R. A., Coulter, L. F., Feick, and L. L., Price, “Changing faces: cosmetics opinion leadership among women in the new hungary,” European Journal of Marketing, vol. 36, no. 11/12, pp. 1287–1308, 2002.Google Scholar
[31] W., Fei-Yue, “Study on cyber-enabled social movement organizations based on social computing and parallel systems,” Journal of University of Shanghai for Science and Technology, vol. 1, p. 003, 2011.Google Scholar
[32] D., Laney, “3D data management: controlling data volume, velocity,” and variety. Technical report, META Group, Tech. Rep., 2001.
[33] F.-Y., Wang, “A big-data perspective on AI: Newton, Merton, and analytics intelligence,” Intelligent Systems, IEEE, vol. 27, no. 5, pp. 2–4, 2012.Google Scholar
[34] D., Centola, “An experimental study of homophily in the adoption of health behavior,” Science, vol. 334, no. 6060, pp. 1269–1272, 2011.Google Scholar
[35] S., Aral and D., Walker, “Identifying influential and susceptible members of social networks,” Science, vol. 337, no. 6092, pp. 337–341, 2012.Google Scholar
[36] S. P., Borgatti and M. G., Everett, “A graph-theoretic perspective on centrality,” Social Networks, vol. 28, no. 4, pp. 466–484, 2006.Google Scholar
[37] L. C., Freeman, “Centrality in social networks conceptual clarification,” Social Networks, vol. 1, no. 3, pp. 215–239, 1979.Google Scholar
[38] P., Bonacich, “Power and centrality: A family of measures,” American Journal of Sociology, pp. 1170–1182, 1987.Google Scholar
[39] P., Bonacich, “Factoring and weighting approaches to status scores and clique identification,” Journal of Mathematical Sociology, vol. 2, no. 1, pp. 113–120, 1972.Google Scholar
[40] M. E., Newman, “The structure and function of complex networks,” SIAM Review, vol. 45, no. 2, pp. 167–256, 2003.Google Scholar
[41] Xindong, Wu, Yi, Li, and Lei, Li, “Influence analysis of online social networks,” Chinese Journal of Computers, vol. 37, no. 4, pp. 1–18, 2014.Google Scholar
[42] M., Gomez Rodriguez, J., Leskovec, and A., Krause, “Inferring networks of diffusion and influence,” in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010, pp. 1019–1028.Google Scholar
[43] A.-L., Barabasi, “The origin of bursts and heavy tails in human dynamics,” Nature, vol. 435, no. 7039, pp. 207–211, 2005.Google Scholar
[44] R. D., Malmgren, D. B., Stouffer, A. E., Motter, and L. A., Amaral, “A poissonian explanation for heavy tails in e-mail communication,” Proceedings of the National Academy of Sciences, vol. 105, no. 47, pp. 18 153–18 158, 2008.Google Scholar
[45] M. S., Granovetter, “The strength of weak ties,” American Journal of Sociology, pp. 1360–1380, 1973.Google Scholar
[46] M., Granovetter, “Economic action and social structure: the problem of embeddedness,” American Journal of Sociology, pp. 481–510, 1985.Google Scholar
[47] P. W., Holland and S., Leinhardt, “Transitivity in structural models of small groups.” Comparative Group Studies, 1971.Google Scholar
[48] D. J., Watts and S. H., Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998.Google Scholar
[49] C. E., Tsourakakis, “Fast counting of triangles in large real networks without counting: algorithms and laws,” in Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, IEEE, 2008, pp. 608–617.Google Scholar
[50] L. C., Freeman, “A set of measures of centrality based on betweenness,” Sociometry, pp. 35–41, 1977.Google Scholar
[51] M., Girvan and M. E., Newman, “Community structure in social and biological networks,” Proceedings of the National Academy of Sciences, vol. 99, no. 12, pp. 7821–7826, 2002.Google Scholar
[52] A., Java, P., Kolari, T., Finin, and T., Oates, “Modeling the spread of influence on the blogosphere,” in Proceedings of the 15th International World Wide Web Conference, 2006, pp. 22–26.Google Scholar
[53] R., Xiang, J., Neville, and M., Rogati, “Modeling relationship strength in online social networks,” in Proceedings of the 19th International Conference on World Wide Web, ACM, 2010, pp. 981–990.Google Scholar
[54] A., Goyal, F., Bonchi, and L. V., Lakshmanan, “Learning influence probabilities in social networks,” in Proceedings of the third ACM International Conference on Web Search and Data Mining, ACM, 2010, pp. 241–250.Google Scholar
[55] P., Singla and M., Richardson, “Yes, there is a correlation:-from social networks to personal behavior on the web,” in Proceedings of the 17th International Conference on World Wide Web, ACM, 2008, pp. 655–664.Google Scholar
[56] R. T. A., Leenders, “Modeling social influence through network autocorrelation: constructing the weight matrix,” Social Networks, vol. 24, no. 1, pp. 21–47, 2002.Google Scholar
[57] K., Saito, R., Nakano, and M., Kimura, “Prediction of information diffusion probabilities for independent cascade model,” in Knowledge-Based Intelligent Information and Engineering Systems, Springer, 2008, pp. 67–75.Google Scholar
[58] K., Saito, M., Kimura, K., Ohara, and H., Motoda, “Selecting information diffusion models over social networks for behavioral analysis,” in Machine Learning and Knowledge Discovery in Databases, Springer, 2010, pp. 180–195.Google Scholar
[59] M., Trusov, A. V., Bodapati, and R. E., Bucklin, “Determining influential users in internet social networks,” Journal of Marketing Research, vol. 47, no. 4, pp. 643–658, 2010.Google Scholar
[60] C., Tan, J., Tang, J., Sun, Q., Lin, and F., Wang, “Social action tracking via noise tolerant timevarying factor graphs,” in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010, pp. 1049–1058.Google Scholar
[61] H., Kwak, C., Lee, H., Park, and S., Moon, “What is twitter, a social network or a news media?” in Proceedings of the 19th International Conference on World Wide Web, ACM, 2010, pp. 591–600.Google Scholar
[62] D. M., Romero, B., Meeder, and J., Kleinberg, “Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter,” in Proceedings of the 20th International Conference on World Wide Web, ACM, 2011, pp. 695–704.Google Scholar
[63] D., Gruhl, R., Guha, D., Liben-Nowell, and A., Tomkins, “Information diffusion through blogspace,” in Proceedings of the 13th International Conference on World Wide Web, ACM, 2004, pp. 491–501.Google Scholar
[64] D., Krackhardt, “The strength of strong ties: the importance of philos in organizations,” Networks and Organizations: Structure, Form, and Action, vol. 216, p. 239, 1992.Google Scholar
[65] L., Liu, J., Tang, J., Han, M., Jiang, and S., Yang, “Mining topic-level influence in heterogeneous networks,” in Proceedings of the 19th ACM International Conference on Information and Knowledge Management, ACM, 2010, pp. 199–208.Google Scholar
[66] P., Cui, F., Wang, S., Liu, et al., “Who should share what?: item-level social influence prediction for users and posts ranking,” in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2011, pp. 185–194.Google Scholar
[67] S. A., Macskassy and M., Michelson, “Why do people retweet? Anti-homophily wins the day!” in ICWSM, 2011.
[68] C., Lee, H., Kwak, H., Park, and S., Moon, “Finding influentials based on the temporal order of information adoption in twitter,” in Proceedings of the 19th International Conference on World Wide Web, ACM, 2010, pp. 1137–1138.Google Scholar
[69] E., Bakshy, B., Karrer, and L. A., Adamic, “Social influence and the diffusion of user-created content,” in Proceedings of the 10th ACM Conference on Electronic Commerce, ACM, 2009, pp. 325–334.Google Scholar
[70] W., Pan, W., Dong, M., Cebrian, et al., “Modeling dynamical influence in human interaction: Using data to make better inferences about influence within social systems,” Signal Processing Magazine, IEEE, vol. 29, no. 2, pp. 77–86, 2012.Google Scholar
[71] G., Ver Steeg and A., Galstyan, “Information transfer in social media,” in Proceedings of the 21st International Conference on World Wide Web, ACM, 2012, pp. 509–518.Google Scholar
[72] G., Ver Steeg and A., Galstyan, “Information-theoretic measures of influence based on content dynamics,” in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, ACM, 2013, pp. 3–12.Google Scholar
[73] J., Huang, X.-Q., Cheng, H.-W., Shen, T., Zhou, and X., Jin, “Exploring social influence via posterior effect of word-of-mouth recommendations,” in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, ACM, 2012, pp. 573–582.Google Scholar
[74] F., Provost, B., Dalessandro, R., Hook, X., Zhang, and A., Murray, “Audience selection for on-line brand advertising: privacy-friendly social network targeting,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2009, pp. 707–716.Google Scholar
[75] A., Goyal, F., Bonchi, and L.V., Lakshmanan, “Discovering leaders from community actions,” in Proceedings of the 17th ACM Conference on Information and Knowledge Management, ACM, 2008, pp. 499–508.Google Scholar
[76] N., Agarwal, H., Liu, L., Tang, and P. S., Yu, “Identifying the influential bloggers in a community,” in Proceedings of the 2008 International Conference on Web Search and Data Mining, ACM, 2008, pp. 207–218.Google Scholar
[77] X., Song, Y., Chi, K., Hino, et al., “Identifying opinion leaders in the blogosphere,” in Proceedings of the Sixteenth ACMConference on Information and KnowledgeManagement, ACM, pp. 971–974, 2007.Google Scholar
[78] F. M., Bass, “A new product growth for model consumer durables,” Management Science, vol. 15, no. 5, pp. 215–227, 1969.Google Scholar
[79] D., Kempe, J., Kleinberg, and É., Tardos, “Maximizing the spread of influence through a social network,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2003, pp. 137–146.Google Scholar
[80] W., Chen, Y., Wang, and S., Yang, “Efficient influence maximization in social networks,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2009, pp. 199–208.Google Scholar
[81] W., Chen, C., Wang, and Y., Wang, “Scalable influence maximization for prevalent viral marketing in large-scale social networks,” in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010, pp. 1029–1038.Google Scholar
[82] J., Leskovec, A., Krause, C., Guestrin, et al., “Cost-effective outbreak detection in networks,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2007, pp. 420–429.Google Scholar
[83] G., Cornuejols, M. L., Fisher, and G. L., Nemhauser, “Exceptional paper-location of bank accounts to optimize float: An analytic study of exact and approximate algorithms,” Management Science, vol. 23, no. 8, pp. 789–810, 1977.Google Scholar
[84] G. L., Nemhauser, L. A., Wolsey, and M. L., Fisher, “An analysis of approximations for maximizing submodular set functions,” Mathematical Programming, vol. 14, no. 1, pp. 265–294, 1978.Google Scholar
[85] J., Hartline, V., Mirrokni, and M., Sundararajan, “Optimal marketing strategies over social networks,” in Proceedings of the 17th International Conference on World Wide Web, ACM, 2008, pp. 189–198.Google Scholar
[86] E., Adar and L. A., Adamic, “Tracking information epidemics in blogspace,” in Web Intelligence, 2005. Proceedings, the 2005 IEEE/WIC/ACM International Conference on, IEEE, 2005, pp. 207–214.Google Scholar
[87] M., Gomez-Rodriguez, J., Leskovec, and A., Krause, “Inferring networks of diffusion and influence,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 5, no. 4, p. 21, 2012.Google Scholar
[88] M. G., Rodriguez, D., Balduzzi, and B., Schölkopf, “Uncovering the temporal dynamics of diffusion networks,” arXiv preprint arXiv:1105.0697, 2011.
[89] S., Wang, X., Hu, P. S., Yu, and Z., Li, “Mmrate: inferring multi-aspect diffusion networks with multi-pattern cascades,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2014, pp. 1246–1255.Google Scholar
[90] M., Gomez Rodriguez, J., Leskovec, and B., Schölkopf, “Structure and dynamics of information pathways in online media,” in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, ACM, 2013, pp. 23–32.Google Scholar
[91] N., Du, L., Song, M., Gomez-Rodriguez, and H., Zha, “Scalable influence estimation in continuous-time diffusion networks,” in Advances in Neural Information Processing Systems, 2013, pp. 3147–3155.
[92] S., Myers and J., Leskovec, “On the convexity of latent social network inference,” in Advances in Neural Information Processing Systems, 2010, pp. 1741–1749.
[93] J., Wallinga and P., Teunis, “Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures,” American Journal of Epidemiology, vol. 160, no. 6, pp. 509–516, 2004.Google Scholar
[94] J. F., Lawless, Statistical Models and Methods for Lifetime Data, JohnWiley & Sons, 2011, vol. 362.Google Scholar
[95] J., Leskovec, M., McGlohon, C., Faloutsos, N. S., Glance, and M., Hurst, “Patterns of cascading behavior in large blog graphs.” in SDM, vol. 7, SIAM, 2007, pp. 551–556.Google Scholar
[96] D., Liben-Nowell and J., Kleinberg, “Tracing information flow on a global scale using internet chain-letter data,” Proceedings of the National Academy of Sciences, vol. 105, no. 12, pp. 4633–4638, 2008.Google Scholar
[97] T., Lappas, E., Terzi, D., Gunopulos, and H., Mannila, “Finding effectors in social networks,” in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010, pp. 1059–1068.Google Scholar
[98] C., Wang, J. C., Knight, and M. C., Elder, “On computer viral infection and the effect of immunization,” in Computer Security Applications, 2000, ACSAC'00, 16th Annual Conference, IEEE, 2000, pp. 246–256.Google Scholar
[99] M., Lipsitch, T., Cohen, B., Cooper, et al., “Transmission dynamics and control of severe acute respiratory syndrome,” Science, vol. 300, no. 5627, pp. 1966–1970, 2003.Google Scholar
[100] L., Hufnagel, D., Brockmann, and T., Geisel, “Forecast and control of epidemics in a globalized world,” Proceedings of the National Academy of Sciences, vol. 101, no. 42, pp. 15 124–15 129, 2004.Google Scholar
[101] D., Brockmann, L., Hufnagel, and T., Geisel, “The scaling laws of human travel,” Nature, vol. 439, no. 7075, pp. 462–465, 2006.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×