Abstract
Mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today’s smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and behavior prediction. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones. In particular, we look at the dynamic learning process over time with various sizes of sampling groups and examine the interplay between these two parameters. We validate our model using extensive simulations carried out using the "Friends and Family" dataset which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community for over a year and is one of the most comprehensive mobile phone datasets gathered in academia to date.
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References
Eagle, N., Pentland, A.: Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing 10, 255–268 (2006)
Aharony, N., et al.: Social fMRI: Investigating and shaping social mechanisms in the real world. In: Pervasive and Mobile Computing (2011)
Lazer, D., et al.: Life in the network: the coming age of computational social science. Science 323, 721 (2009)
Barabasiand, A.-L., Albert, R.: Emergence of scaling in random networks. Science (1999)
Newman, M.E.J.: The structure and function of complex networks.
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature (1998)
Eagle, N., Pentland, A., Lazer, D.: From the Cover: Inferring friendship network structure by using mobile phone data. Proceedings of The National Academy of Sciences 106(36), 15274–15278 (2009)
Gonzalez, M.C., Hidalgo, A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature (2008)
Networks, S.: http://www.sensenetworks.com/
Madan, A., et al.: Social sensing for epidemiological behavior change. In: Ubiquitous Computing/Handheld and Ubiquitous Computing, pp. 291–300 (2010)
Madan, A., Farrahi, K., Gatica-Perez, D.: Pervasive Sensing to Model Political Opinions in Face-to-Face Networks (2011)
Montoliu, R., Gatica-Perez, D.: Discovering human places of interest from multimodal mobile phone data, pp. 1–10 (2010)
Lu, H., et al.: The Jigsaw continuous sensing engine for mobile phone applications, in Conference on Embedded Networked Sensor Systems, pp. 71–84 (2010)
Joki, A., Burke, J.A., Estrin, D.: Campaignr: A Framework for Participatory Data Collection on Mobile Phones (2007)
Abdelzaher, T.F., et al.: Mobiscopes for Human Spaces. IEEE Pervasive Computing 6(2), 20–29 (2007)
Olguín, D.O., et al.: Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior. IEEE Transactions on Systems, Man, and Cybernetics 39(1), 43–55 (2009)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58(7), 1019–1031 (2007)
Mislove, A., et al.: You are who you know: inferring user profiles in online social networks. In: Web Search and Data Mining, pp. 251–260 (2010)
Rokach, L., et al.: Who is going to win the next Association for the Advancement of Artificial Intelligence Fellowship Award? Evaluating researchers by mining bibliographic data. Journal of the American Society for Information Science and Technology (2011)
Funf. Funf Project, http://funf.media.mit.edu
Hagberg, A.A., Schult, D.A., Swart, P.J.: Exploring Network Structure, Dynamics, and Function using Network X (2008)
Hall, M., et al.: The WEKA data mining software: an update. Sigkdd Explorations 11(1), 10–18 (2009)
Blondel, V.D., et al.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 10 (2008)
Xie, J., Szymanski, B.K.: Community Detection Using A Neighborhood Strength Driven Label Propagation Algorithm. Computing Research Repository, abs/1105.3 (2011)
Rouvinen, P.: Diffusion of digital mobile telephony: Are developing countries different? Telecommunications Policy 30(1), 46–63 (2006)
Erickson, G.M.: Tyrannosaur Life Tables: An Example of Nonavian Dinosaur Population Biology. Science 313(5784), 213–217 (2006)
Donofrio, A.: A general framework for modeling tumor-immune system competition and immunotherapy: Mathematical analysis and biomedical inferences. Physica D-nonlinear Phenomena 208(3-4), 220–235 (2005)
Pan, W., Aharony, N., Pentland, A.: Composite Social Network for Predicting Mobile Apps Installation. In: Intelligence, AAAI 2011, San Francisco, CA (2011)
Krishnamurthy, B., Wills, C.E.: On the leakage of personally identifiable information via online social networks. Computer Communication Review 40(1), 7–12 (2009)
Binde, B.E., McRee, R., O‘Connor, T.J.: Assessing Outbound Traffic to Uncover Advanced Persistent Threat, Sans Institute (2011)
Solutionary, White Paper: The Advanced Persistent Threat, APT (2011)
Brunner, M., et al.: Infiltrating Critical Infrastructures with Next-Generation Attacks. Fraunhofer-Institute for Secure Information Technology SIT Munich (2010)
Kalmijn, M.: Intermarriage and Homogamy: Causes, Patterns, Trends. Annual Review of Sociology 24(1), 395–421 (1998)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology 27(1), 415–444 (2001)
Dey, A.K., et al.: Getting Closer: an Empirical Investigation of the Proximity of Users to Their Smart Phones. In: Proc. of the 13th International Conference on Ubiquitous Computing, pp. 163–172 (2011)
Altshuler, Y., Aharony, N., Elovici, Y., Pentland, A., Cebrian, M.: Stealing Reality: When Criminals Become Data Scientists (or Vice Versa). IEEE Intelligent Systems 26(6), 22–30 (2011)
Altshuler, Y., Fire, M., Aharony, N., Elovici, Y., Pentland, A(S.): How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 43–52. Springer, Heidelberg (2012)
Altshuler, Y., Fire, M., Aharony, N., Elovici, Y., Pentland, A.: Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data, Arxiv preprint arXiv:1111.4645 (2011)
Altshuler, Y., Wagner, I.A., Bruckstein, A.M.: On Swarm Optimality in Dynamic and Symmetric Environments. Economics 7, 11–18 (2008)
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Altshuler, Y., Fire, M., Aharony, N., Volkovich, Z., Elovici, Y., Pentland, A.(. (2013). Trade-Offs in Social and Behavioral Modeling in Mobile Networks. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_45
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DOI: https://doi.org/10.1007/978-3-642-37210-0_45
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