Some Comments on the Some Comments on the Foundations of Network - - PowerPoint PPT Presentation

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Some Comments on the Some Comments on the Foundations of Network - - PowerPoint PPT Presentation

Some Comments on the Some Comments on the Foundations of Network Analysis Foundations of Network Analysis Carter T. Butts Carter T. Butts Department of Sociology and Department of Sociology and Institute for Mathematical Behavioral Sciences


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Some Comments on the Some Comments on the Foundations of Network Analysis Foundations of Network Analysis

Carter T. Butts Carter T. Butts

Department of Sociology and Department of Sociology and Institute for Mathematical Behavioral Sciences Institute for Mathematical Behavioral Sciences University of California, Irvine University of California, Irvine

Prepared for the August 25, 2009 UCI MURI AHM. This work was Prepared for the August 25, 2009 UCI MURI AHM. This work was supported by DOD ONR award N00014-8-1-1015. supported by DOD ONR award N00014-8-1-1015.

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Background Background

 Many foundational

Many foundational issues in network issues in network analysis analysis

Vertex, edge set definition, Vertex, edge set definition, time scales, etc. time scales, etc.

 Often taken for granted

Often taken for granted

Things "everyone knows" Things "everyone knows"

  • but impact is not well-
  • but impact is not well-

understood! understood!

 Today: some comments

Today: some comments from a recent review, and from a recent review, and thoughts on how this thoughts on how this affects our work affects our work

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Choosing the Vertex Set Choosing the Vertex Set

 Most basic issue - whence

Most basic issue - whence the vertex set? the vertex set?

 Not always obvious

Not always obvious

Selection/boundary issues Selection/boundary issues

Choice of scale in multiscale Choice of scale in multiscale systems systems

Subordination among Subordination among

  • rganizations
  • rganizations

Containment/ recombination Containment/ recombination in households in households

 Different choices can

Different choices can greatly affect network greatly affect network measures measures

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Effect of Vertex Aggregation Effect of Vertex Aggregation

Katrina EMON Data (Butts et al., 2009)

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Valued Edges and Thresholding Valued Edges and Thresholding

 Well-recognized (but not well-

Well-recognized (but not well- understood) issue: dealing understood) issue: dealing with valued edges with valued edges

Most concepts, models Most concepts, models dichotomous, but not all relations dichotomous, but not all relations are are

 Usual approach is

Usual approach is thresholding, but this has non- thresholding, but this has non-

  • bvious consequences....
  • bvious consequences....

Can be reasonable if edge Can be reasonable if edge behavior sigmoidal and threshold behavior sigmoidal and threshold is well-chosen is well-chosen

Otherwise, same data can lead to Otherwise, same data can lead to completely different results completely different results

Edge Value Phenomenal Impact

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Effect of Threshold Selection Effect of Threshold Selection

(C. Elegans Data (Watts, Strogatz, 1998)

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Edge Timing and Network Edge Timing and Network Processes Processes

Often, networks assumed as Often, networks assumed as substrate for a social process substrate for a social process

May need to consider network May need to consider network dynamics... dynamics...

Edge, vertex turnover Edge, vertex turnover

...but nature of dynamics ...but nature of dynamics depends on depends on relative relative time time scales of network, process scales of network, process evolution evolution

Not whether network "is" static Not whether network "is" static

  • r dynamic in isolation
  • r dynamic in isolation

Right model can vary from fixed Right model can vary from fixed network to random mixing network to random mixing

Different models for different Different models for different purposes purposes

Edge Time Scale Process Time Scale

Evolution on Fixed Graph Random Mixing Process/Network Coevolution Episodic Interaction (Hazard Based)

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Illustration: Diffusion on an Illustration: Diffusion on an Evolving Network Evolving Network

 Common process of

Common process of interest: diffusion interest: diffusion

Simple example: Simple example:

Once "infected," vertices Once "infected," vertices "infect" neighbors w/iid "infect" neighbors w/iid exponential waiting times exponential waiting times

Process continues until all Process continues until all no available hosts left no available hosts left

Adding edge dynamics Adding edge dynamics

Each relationship begins Each relationship begins after iid exp waiting time, after iid exp waiting time, has iid exp duration has iid exp duration

Intuition: edge dynamics Intuition: edge dynamics affect permeability of affect permeability of network to diffusion network to diffusion

 How does edge timing

How does edge timing affect diffusion? affect diffusion?

Illustrative simulation: Illustrative simulation:

Two sample networks Two sample networks

Mean duration, std dev of Mean duration, std dev of

  • nset time varied
  • nset time varied

Poisson diffusion on Poisson diffusion on dynamic network (starting dynamic network (starting at time 0) w/unit infection at time 0) w/unit infection rate rate

Basic outcome: expected Basic outcome: expected fraction of population fraction of population infected by a single, random infected by a single, random "seed" "seed"

How powerful are timing How powerful are timing effects? effects?

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Sexual Contact Data (Potterat et al., 2002) WTC Radio Data (Butts et al., 2007)

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Sexual Contact Data (Potterat et al., 2002) WTC Radio Data (Butts et al., 2007)

  • Time scales

determine diffusion behavior

  • Three basic

regimes

  • Near-complete

diffusion

  • Incomplete

diffusion

  • Minimal

diffusion

  • Behavior

similar across networks

  • Differences in

degree distribution, clustering, cohesion matter less than timing!

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Some Conclusions and Project- Some Conclusions and Project- Related Comments Related Comments

 General conclusions:

General conclusions:

Need to be attentive to the Need to be attentive to the basics basics

"Any old network" may be "Any old network" may be OK for algorithm testing, OK for algorithm testing, but not for serious analysis but not for serious analysis

Need to learn more about Need to learn more about robustness of methods to robustness of methods to "network specification "network specification error" error"

May need models for May need models for alternate data alternate data representations representations

 Project-specific

Project-specific recommendations: recommendations:

Simple models for valued Simple models for valued data? data?

"Threshold regression" "Threshold regression" ERGMs? ERGMs?

Models for vertices Models for vertices w/containment or w/containment or hierarchical structure hierarchical structure

Not sure that block- Not sure that block- hierarchical ERGMs hierarchical ERGMs enough, but a start enough, but a start

Keep pushing on Keep pushing on dynamics! dynamics!