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COMP522 - Project Presentation Modelling Information Diffusion over - - PowerPoint PPT Presentation

Sep 05, 2023 172 likes •406 views

COMP522 - Project Presentation Modelling Information Diffusion over Networks using DEVS By: Hiu Kim, Yuen Background Information Diffusion over network @ipad 4 T = 0 Background Information Diffusion over network @ipad 4 @ipad 4 @ipad 4

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COMP522 - Project Presentation

Modelling Information Diffusion over Networks using DEVS

By: Hiu Kim, Yuen

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Information Diffusion over network

@ipad 4

T = 0

Background

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Information Diffusion over network

@ipad 4

T = t

@ipad 4 @ipad 4 @ipad 4

Background

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What are we interested in?

Background

  • Speed of the spread
  • # of diffused nodes at the end
  • Any difference if we:

○ start at different node? ○ with other network topology?

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Previous Work

Background

Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter

by: Daniel. M. Romero, Brandan Meeder and Jon Kleinberg from cornell university

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What is this project?

Model

Work from Daniel. M. Romero, Brandan Meeder and Jon Kleinberg Work from me

Simplified Model DEVS Model (PythonDEVS) Simulation/ Experiments

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Network Diffusion Model Simulation Process

T = 0

Unaffected Affected

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Network Diffusion Model Simulation Process

Unaffected Affected Activating at simulation step 1:

P(0) P(0) P(0) P(0) P(0) P(0)

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Network Diffusion Model Simulation Process

1 1 1 T = 1

Unaffected Affected Activating Newly Affected K Unaffected - Failed k times

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Network Diffusion Model Simulation Process

1 1 1

Unaffected Affected Activating Newly Affected K Unaffected - Failed k times at simulation step 2:

P(0) P(0) P(1)

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Network Diffusion Model Simulation Process

1 1 1 2 T = 2

Unaffected Affected Activating Newly Affected K Unaffected - Failed k times

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Network Diffusion Model Simulation Process

1 1 1 2

Unaffected Affected Activating Newly Affected K Unaffected - Failed k times at simulation step 3:

P(2) P(0)

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Network Diffusion Model Simulation Process

1 1 1 1 T = 3

Unaffected Affected Activating Newly Affected K Unaffected - Failed k times

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DEVS Model - Node as an AtomicDEVS

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DEVS Model - Interaction between Nodes

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DEVS Model - PythonDEVS Implementation

text file python function

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What are we interested in?

Revisited

  • Speed of the spread
  • # of diffused nodes at the end
  • Any difference if we:

○ start at different node? ○ with other network topology?

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Different Network Topology

Background

Flat Random Small World property Scale-Free

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Experiments and Results (1) - network topology

Flat Random - P(K) = 0.5 Scale Free - P(K) = 0.5

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Experiments and Results (2) - activation probability

Scale Free - P(K) = 0.2 Scale Free - P(K) = 0.8

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Experiments and Results (3) - information origin

  • riginated at

lowest degree node

  • riginated at

highest degree node

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  • Use realistic inputs

○ real network topology - e.g. social network? ○ estimate parameters - e.g. P(0), P(1)

  • Build a comprehensive tool for real use

Conclusions

  • "Network Science" Model -> DEVS Model
  • An simulation environment with PythonDEVS

○ Take parameters and produce (useful?) output

Future Work

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Thanks!