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Automatic mapping and modeling of human networks

Automatic mapping and modeling of human networks. ALEX (SANDY) PENTLAND THE MEDIA LABORATORY CAMBRIDGE PHYSIC A: STATISTICAL MECHANICS AND ITS APPLICATIONS 2007. Outline. 1. Introduction 2. Socioscopes 3. Reality mining 4. Social signals 5. Practical concerns 6. Conclusions Comments.

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Automatic mapping and modeling of human networks

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  1. Automatic mapping and modeling of human networks ALEX (SANDY) PENTLAND THE MEDIA LABORATORY CAMBRIDGE PHYSIC A: STATISTICAL MECHANICS AND ITS APPLICATIONS 2007

  2. Outline • 1. Introduction • 2. Socioscopes • 3. Reality mining • 4. Social signals • 5. Practical concerns • 6. Conclusions • Comments

  3. 1. Introduction (1/2) • Studies on office interactions [1] • 35–80% of work time in conversation, • 14–93% of work time in communication • 7–82% of work time in meetings • The properties of human networks : • Location context: work, home, etc. • Social context: with friends, co-workers, boss, family, etc. • Social interaction: are you displaying interest, boredom etc. • To obtain solid, dynamic estimates of the users’ group membership and the character of their social relationships. [1] T. Allen, Architecture and Communication Among Product Development Engineers, MIT Press, Cambridge, MA, 1997, pp. 1–35.

  4. 1. Introduction (2/2) • Using this data to model individual behavior as a stochastic process • allows prediction of future activity. • The key to automatic inference of information network parameters is the recognition • Standard methods, surveys • subjectivity and memory effects, out-of-date. • Even information is available, need to validate or correct by automatic method • we present statistical learning methods • wearable sensor data to estimates user’s interaction

  5. 2. Socioscopes • mapping and modeling human networks • the conceptual framework used in biological observation, • such as apes in natural surroundings • natural experiments • such as twin studies, • but replacing expensive and unreliable human observations with automated, computer-mediated observations. • accurately and continuously track the behavior • recording with near perfect accuracy.

  6. Imaginary Socioscope • Using mobile telephones, electronic badges, and PDAs • Tracking the behavior of 94 people in two divisions of MIT • the business school and the Media Laboratory • between 23 and 39 years of age • the business school students a decade older than the Media Lab students. • 2/3 male and 1/3 female • half were raised in America.

  7. Three main parts of the Socioscope • The first part: ‘smart’ phones • to observe gross behavior (location, proximity) continuously over months • 330,000 h of data , the behavior of 94 people, 35 years • The second part: electronic badges • record the location, audio, and upper body movement • to observe for fine-grained behavior (location, proximity, body motion) over one-day periods • The third part: a microphone and software • to analyze vocalization statistics with an accuracy of tenths of seconds

  8. 2. Socioscopes (4/5) • Four main types of analysis: • characterization of individual and group distribution and variability • using an Eigenvector or principal components analysis • conditional probability relationships between individual behaviors known as ‘influence modeling’ • accuracy with which behavior can be predicted • with equal types I and II error rates • comparison of these behavioral measures to standard human network parameters.

  9. 3. Reality mining • Eigenbehaviors provide an efficient method for learning and classifying user behavior [9]. • Given behaviors Γ1, Γ2, . . . ,Γm for a group of M people, • the average behavior of the group can be defined by • To deviate an individual’s behaviorfrom the mean. • A set of M vectors, Φ = Γi - Ψ, [9] N. Eagle, A. Pentland, Eigenbehaviors: Identifying Structure in Routine, October 2005, see TR 601 hhttp://hd.media.mit.edui.

  10. Fig. 1 • Γi(x,y), 2-D location information • a low-dimensional ‘behavior space’, • spanned by their Eigenbehaviors

  11. 3.1. Eigenbehavior modeling • Principle Components Analysis, PCA • a set M orthonormalvectors, un, which best describes the distribution of the set of behavior data when linearly combined with their respective scalar values, λ n. • Covariance matrix of Φ • Where • The Eigenbehaviors can be ranked by the total amount of variance in the data for which they account, the largest associated Eigenvalues.

  12. 3.2. Human Eigenbehaviors (1/2) • The main daily pattern, observed • subjects leaving their sleeping place to spend time in a small set of locations during the daylight hours • breaking into small clusters to move to one of a few other buildings during the early night hours and weekends • then back to their sleeping place. • Over 85% of the variance in the behavior of low entropy subjects can be accounted for by the mean vector alone.

  13. 3.2. Human Eigenbehaviors (2/2) • the top three Eigen behavior components • the weekend pattern, • the working late pattern, and • the socializing pattern. • The ability to accurately characterize peoples’ behavior with a low-dimensional model means • automatically classify the users’ location context • the system to request that the user label locations • can achieve very high accuracies with limited user input.

  14. 3.3. Learning influence (1/2) • Behavioral structure • Conditional probability to predict the behavior • Two main sub-networks • during the day • in the evening • Critical requirement for automatic mapping and modeling of human networks • to learn and categorize user behavior • accurately capture the dynamics of the network.

  15. 3.3. Learning influence (2/2) • Coupled Hidden Markov Models, CHMMs [10-12] • to describe interactions between two people • the interaction parameters • limited to the inner products of the individual Markov chains. • The graphical model for influence model • behavior has the same first-order Eigen structure • it possible to analyze global behavior [10] A. Pentland, T. Choudhury, N. Eagle, S. Push, Human Dynamics: Computation for Organizations, Pattern Recognition, vol. 26, 2005, pp. 503–511, see TR 589 hhttp://hd.media.mit.edui. [11] W. Dong, A. Pentland, Multi-sensor data fusion using the influence model, IEEE Body Sensor Networks, April, Boston, MA, 2006, see TR 597 hhttp://hd.media.mit.edui. [12] C. Asavathiratham, The influence model: a tractable representation for the dynamics of networked Markov chains, in: Department of EECS, 2000, MIT, Cambridge.

  16. 3.4. Influence modeling • Using the influence model to analyze the proximity data from our cell phone experiment • we find that Clustering the daytime influence relationships • 96% accuracy at identifying workgroup affiliation • 92% accuracy at identifying self-reported ‘close’ friendships. • Similar findings, using the badge platform. • the combination of influence and proximity predicted whether or not two people were affiliated with the same company with 93% accuracy [6].

  17. 4. Social signals • People are able to ‘size up’ other people from a very short period of observation [13, 14]. • linguistic information from observation, • to accurately judge prospects for friendship, work relationship, negotiation, marital prospects • we developed methods for automatically measuring some of the more important types of social signaling [7]. • Excitement, freeze, determined and accommodating.

  18. Predict human behavior • Can predict human behavior? • without listening to words or knowing about the people involved. • By linear combinations of social signal features to accurately predict human behaviors. • who would exchange business cards at a meeting; • which couples would exchange phone numbers at a bar; • who would come out ahead in a negotiation; • who was a connector within their work group;

  19. 5. Practical concerns • Continuous analysis interactions within an organization may seem reasonable and if misused, could be potentially dangerous. • Conversation postings: • the data should be shared, private, or permanently deleted. • Decided by individuals. • Demanding environments: • the environmental demands may supersede privacy concerns.

  20. 6. Conclusions • human behavior is predictable than is generally thought, and especially predictable from others. • This suggests that • humans are best thought of social intelligences rather than independent actors. • As a consequence • can analyze behavior using statistical learning tools • such as Eigenvector analysis and influence modeling, • to infer social relationships without to understand the detailed linguistic or cognitive structures surrounding social interactions.

  21. Comments • 經由human network 找出人與人間的關係及其建立model的作法 • 在我們的運用是找出criminal及找criminal的同伙 • 瞭解將行動電話及不同的sensor等如何運用在human network • Human network 對 prediction的幫助為何?

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