automatic mapping and modeling of human networks alex (sandy) pentland the media laboratory...
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Automatic mapping and modeling of human networks
ALEX (SANDY) PENTLAND
THE MEDIA LABORATORY CAMBRIDGE
PHYSIC A: STATISTICAL MECHANICS AND ITS APPLICATIONS
2007
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1. Introduction2. Socioscopes3. Reality mining4. Social signals5. Practical concerns6. ConclusionsComments
Outline2
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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. Introduction (1/2)3
[1] T. Allen, Architecture and Communication Among Product Development Engineers, MIT Press, Cambridge, MA, 1997, pp. 1–35.
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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Standard methods, surveys subjectivity and memory effects, out-of-date.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
1. Introduction (2/2)4
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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.
2. Socioscopes5
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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.
Imaginary Socioscope6
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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
yearsThe 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
Three main parts of the Socioscope
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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.
2. Socioscopes (4/5)8
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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 behavior from the mean. A set of M vectors, Φ = Γi - Ψ,
3. Reality mining9
[9] N. Eagle, A. Pentland, Eigenbehaviors: Identifying Structure in Routine, October 2005, see TR 601 hhttp://hd.media.mit.edui.
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Γi(x,y), 2-D location information a low-dimensional
‘behavior space’, spanned by their
Eigenbehaviors
Fig. 110
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Principle Components Analysis, PCA a set M orthonormal vectors, 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.
3.1. Eigenbehavior modeling11
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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.
3.2. Human Eigenbehaviors (1/2)12
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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.
3.2. Human Eigenbehaviors (2/2)13
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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.
3.3. Learning influence (1/2)14
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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
3.3. Learning influence (2/2)15
[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.
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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].
3.4. Influence modeling16
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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.
4. Social signals17
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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;
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Predict human behavior
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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.
5. Practical concerns19
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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.
6. Conclusions20
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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 ?
Comments 21