a computational framework for analysis of dynamic social networks

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A Computational Framework for Analysis of Dynamic Social Networks Tanya Berger-Wolf University of Illinois at Chicago Joint work with Jared Saia University of New Mexico

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A Computational Framework for Analysis of Dynamic Social Networks. Tanya Berger-Wolf University of Illinois at Chicago. Joint work with Jared Saia University of New Mexico. Zebras. Dan Rubenstein, Siva Sandaresan, Ilya Fischhoff (Princeton). - PowerPoint PPT Presentation

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Page 1: A Computational Framework for Analysis of Dynamic Social Networks

A Computational Framework for Analysis

of DynamicSocial Networks

Tanya Berger-WolfUniversity of Illinois

at Chicago

Joint work withJared Saia

University of New Mexico

Page 2: A Computational Framework for Analysis of Dynamic Social Networks

ZebrasDan Rubenstein, Siva Sandaresan, Ilya Fischhoff (Princeton)

Movie credit: “Champions of the Wild”, Omni-Film Productions.

Page 3: A Computational Framework for Analysis of Dynamic Social Networks

Ants Stephen Pratt (Princeton)

Page 4: A Computational Framework for Analysis of Dynamic Social Networks

People – Hidden GroupsBaumes et al. (RPI)

Page 5: A Computational Framework for Analysis of Dynamic Social Networks

Context• disease modeling

Eubank et.al.‘04, Keeling’99, Kretzschmar&Morris’96 • cultural and information transmission

Baumes et.al.’04, Broido&Claffy’01, Carley’96, Chen&Carley’05, Kempe et.al.’03, Tsvetovat et.al.’03,Tyler et.al.’03, Wellman’97

• intelligence and surveillanceAiroldi&Malin’04,Baumes et.al.’04, Kolata’05, Malin’04, Magdon-Ismail et.al.’03

• business managementBernstein et.al.’02, Carley&Prietula’01, Papadimitriou’97, Papadimitriou&Servan-Schreiber’99

• conservation biology and behavioral ecologyCroft et.al.’04, Cross et.al.’05, Lusseau&Newman’04

Page 6: A Computational Framework for Analysis of Dynamic Social Networks

Social Networks: Static vs Dynamic

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1/31/3 IndividualsStrength or probability

of interaction over a period of time

Page 7: A Computational Framework for Analysis of Dynamic Social Networks

Advantage of Dynamic Networks:•More accurate information•Time related questions:– How do processes spread through

population? – Who are the individuals that change the

dynamics of interaction (leaders, interaction facilitators, etc.)? How do they emerge?

– How do social structures change with outside circumstances?

– What is the average lifespan of a social structure and are there recurring structures?

Page 8: A Computational Framework for Analysis of Dynamic Social Networks

Input – Individual Information

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Page 9: A Computational Framework for Analysis of Dynamic Social Networks

Individual Information Input – Problem:

Objects within a cluster are closer to eachother than to objects in other clusters

Page 10: A Computational Framework for Analysis of Dynamic Social Networks

Input – Pairwaise InformationBaumes et al.(RPI) and Washington Post

PentagonPennsylvaniaWTC NorthWTC South

Jan-Dec 2000Jan-Apr 2001May-Jul 2001Aug-Sep 2001

Page 11: A Computational Framework for Analysis of Dynamic Social Networks

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Page 12: A Computational Framework for Analysis of Dynamic Social Networks

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Page 13: A Computational Framework for Analysis of Dynamic Social Networks

Theseus’s Paradox• During a twelve month period 95% of all

the atoms that make up your 50 trillion cells are replaced

• FAA regulations: airplane = left rudder number

• Ship of Theseus"The ship wherein Theseus and the youth of Athens returned [from Crete] had thirty oars, and was preserved by the Athenians down even to the time of Demetrius Phalereus, for they took away the old planks as they decayed, putting in new and stronger timber in their place, insomuch that this ship became a standing example among the philosophers, for the logical question of things that grow; one side holding that the ship remained the same, and the other contending that it was not the same."

Page 14: A Computational Framework for Analysis of Dynamic Social Networks

A group persist in time (is a metagroup) if some (big)

fraction β of it exists some (big) fraction α of time

•A time snapshot is a partition g1t…gmt

•Similarity measure

•Metagroup is a path of length ≥ α with edges of weight ≥ β

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Page 15: A Computational Framework for Analysis of Dynamic Social Networks

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Page 16: A Computational Framework for Analysis of Dynamic Social Networks

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Page 17: A Computational Framework for Analysis of Dynamic Social Networks

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Page 18: A Computational Framework for Analysis of Dynamic Social Networks

Simple Stats:• Metagroup = path length ≥ α• Total #metagroups = #paths length ≥

α• Maximal metagroup length = max path

length• Most persistent metagroup = longest

path in a DAG• Let x be a member of MG is it appears

in it at least γ times.Largest metagroup = dynamic programming on membership set.

Page 19: A Computational Framework for Analysis of Dynamic Social Networks

Group ConnectivityGiven groups g1,…,gl, are they in the same metagroup?

g1 gl-1g2 gl

Most persistent/largest/loudest/.. metagroup that contains these groups

A metagroup that contains largest number of these groups – dynamic programming

Page 20: A Computational Framework for Analysis of Dynamic Social Networks

Individual ConnectivityGiven individuals S={s1,…,sl}, are

they in the same metagroup?

•Metagroup that contains max number of individuals in S

•Most persistent/largest/shiniest.. metagroup that contains all individuals in S

Page 21: A Computational Framework for Analysis of Dynamic Social Networks

Critical Group SetThe smallest set of groups

whose absence leaves no metagroups (for given α and β)

Formally: remove fewest vertices in a DAGso there are no paths of length > k-1

K-path Vertex Shattering Set

Page 22: A Computational Framework for Analysis of Dynamic Social Networks

K-path Vertex Shattering SetNP-hard: 2-path shattering

set = independent set

?Polynomial: T-path shattering

set (T is the longest path length) – min vertex cut in a DAG

k=2

k=T

Page 23: A Computational Framework for Analysis of Dynamic Social Networks

Critical Individual SetThe smallest set of individuals

whose absence leaves no metagroups (for given α and β)

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Page 24: A Computational Framework for Analysis of Dynamic Social Networks

Other questions:•Close Group: individuals that appear together more than others.•Loyal Individuals: appear most frequently in any metagroup.•Individual Membership: metagroup which maximizes the cardinality of the set of groups in which a given individual occurs.Extra/Introvert: member of the largest/smallest number of metagroups.

•Metagroup Representative: an individual who occurs more in a metagroup than any other individual and occurs in it more than in any other metagroup.

•Demographic Distinction: given a coloring of individuals, is there a property that distinguishes one color from the others?

•Critical Parameter Values: largest values of α, β for which there exists at least k metagroups. Largest γ for which each metagroup has at least k members.

•Sampling Rate: largest time step such that the answer does not change if the time step is decreased but changes if it is increased.

•Critical Time Moments: e.g., the time when the groups' membership changes most, i.e. minimal edge weight sum between time steps.

•Data Augmented Solution Reconciliation: given partial sets of observations and a partial solution, find is the combined solution to the entire input.

Page 25: A Computational Framework for Analysis of Dynamic Social Networks

Conclusions

• New data structure with explicit time component of social interactions

• Generic – applicable in many contexts

• Powerful – can ask meaningful questions (finding leaders of zebras)

• But! (And?) many hard algorithmic questions – lots of work!

Page 26: A Computational Framework for Analysis of Dynamic Social Networks

Credits:Jared SaiaDan RubensteinSiva SundaresanIlya FischoffSimon LevinS. Muthu MuthukrishnanMartin Pal