Automatic mapping and modeling of human networks

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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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Outline

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

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1. Introduction (1/2)

• Studies on office interactions 1
• 3580 of work time in conversation,
• 1493 of work time in communication
• 782 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. 135.
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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• subjectivity and memory effects, out-of-date.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 users
  interaction

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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.

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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.

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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

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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.

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3. Reality mining

• Eigenbehaviors provide an efficient method for
  learning and classifying user behavior 9.
• Given behaviors G1, G2, . . . ,Gm for a group of
  M people,
• the average behavior of the group can be defined
  by
• To deviate an individuals behavior from the
  mean.
• A set of M vectors, F Gi - ?,

9 N. Eagle, A. Pentland, Eigenbehaviors
Identifying Structure in Routine, October 2005,
see TR 601 hhttp//hd.media.mit.edui.
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Fig. 1

• Gi(x,y), 2-D location information
• a low-dimensional behavior space,
• spanned by their Eigenbehaviors

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3.1. Eigenbehavior modeling

• 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, ? n.
• Covariance matrix of F
• Where
• The Eigenbehaviors can be ranked by the total
  amount of variance in the data for which they
  account, the largest associated Eigenvalues.

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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.

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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.

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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.

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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. 503511, 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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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.

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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.

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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

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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.

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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.

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Comments

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