facebook networks analysis

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Facebook Networks Analysis Mojtaba Khodadadi Advisor: Dr. Farhad Shahbazi May 2014

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This is a presentation of the analysis of 8 Facebook networks. The data of these networks was generated by NetVizz app in Facebook. The analysis was performed by Gephi. This project was done as the final project of "Advanced Statistical Mechanics 2" course with Dr. Farhad Shahbazi at Isfahan University of Technology - Spring 2014

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Page 1: Facebook Networks Analysis

Facebook Networks

Analysis

Mojtaba Khodadadi Advisor: Dr. Farhad Shahbazi

May 2014

Page 2: Facebook Networks Analysis

• Gephi: is an interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs.

Introduction

Page 3: Facebook Networks Analysis

Introduction

Page 4: Facebook Networks Analysis

• Netvizz: is a tool that extracts data from different sections of the Facebook platform (personal profile, groups, pages) for research purposes. File outputs can be easily analyzed in standard software.

Introduction

Page 5: Facebook Networks Analysis

Introduction

Page 6: Facebook Networks Analysis

• Density: Measures how close the network is to complete. for complete graph density=1.

• Average Clustering Coefficient: The mean value of individual coefficients.

• Diameter: The longest graph distance between any two nodes in the network.

• Betweenness Centrality: Measures how often a node appears on shortest paths between nodes in the network.

Definitions

Page 7: Facebook Networks Analysis

Definitions

• Closeness Centrality: The average distance from a given starting node to all other nodes in the network.

• Eccentricity: The distance from a given starting node to the farthest node from it in the network.

• Clustering Coefficient: Indicates how nodes are embedded in their neighborhood. (“What is the probability that two of my friends are also friends?”)

Page 8: Facebook Networks Analysis

• Type: page like network

• Likes: 631,000

page like network: This module starts with a selected page (the "seed") and retrieves all the pages that page likes. It will continue until the specified crawl depth is reached (currently limited to 2). The output is a network containing a (directed) network of pages.

First Network: “ ”

Page 9: Facebook Networks Analysis

First Network: “ ”

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• Nodes: 34

• Edges: 72

• Directed

• Unweighted

• Avg degree: 2.118

• Graph density: 0.064

• Diameter: 4

• Avg path length: 2.632

• # shortest paths: 443

• Modularity: 0.66

• # communities: 5

• Avg clustering coef: 0.663

• Total Triangles: 30

First Network: “ ”

Page 11: Facebook Networks Analysis

• Degree distribution

First Network: “ ”

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• Betweenness Centrality distribution

First Network: “ ”

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• Closeness Centrality distribution

First Network: “ ”

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• Eccentricity distribution

First Network: “ ”

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First Network: “ ”

• Nodes are ranked by betweenness centrality

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• Clustering coefficient distribution

First Network: “ ”

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• Nodes are ranked by clustering coefficient

First Network: “ ”

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• Communities size distribution

First Network: “ ”

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First Network: “ ”

• Nodes are ranked by betweenness centrality

Page 20: Facebook Networks Analysis

• Type: like network

like network: This module creates a network from your friends and their likes (both users and liked objects are nodes). Only liked pages are taken into account, not external objects.

Second Network: “my facebook like network”

Page 21: Facebook Networks Analysis

Second Network: “my facebook like network”

Page 22: Facebook Networks Analysis

• Nodes: 8559

• Edges: 14015

• Directed

• Unweighted

• Avg degree: 1.637

• Graph density: 0.000

• Diameter: 1

• Avg path length: 1.0

• # shortest paths: 14015

• Modularity: 0.578

• # communities: 38

• Avg clustering coef: 0

• Total Triangles: 0

Second Network: “my facebook like network”

Page 23: Facebook Networks Analysis

• Type: personal friend network

personal friend network: This module creates a network with all the friendship connections in your personal network

Third Network: “my facebook friends network”

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Third Network: “my facebook friends network”

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• Nodes: 68

• Edges: 620

• Directed

• Unweighted

• Avg degree: 7.647

• Graph density: 0.114

• Diameter: 5

• Avg path length: 1.825

• # shortest paths: 1524

• Modularity: 0.209

• # communities: 7

• Avg clustering coef: 0.548

• Total Triangles: 1833

Third Network: “my facebook friends network”

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• Type: group

• Members: 3597

Group network: This module creates a network with all the friendship connections in the group.

Forth Network: “IUT” group

Page 27: Facebook Networks Analysis

Forth Network: “IUT” group

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• Nodes: 3597

• Edges: 30691

• Directed

• Unweighted

• Avg degree: 8.532

• Graph density: 0.002

• Diameter: 14

• Avg path length: 3.766

• # shortest paths: 2701550

• Modularity: 0.564

• # communities: 304

• Avg clustering coef: 0.285

• Total Triangles: 74277

Forth Network: “IUT” group

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• Degree distribution => scale-free network

Forth Network: “IUT” group

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• Type: group

• Members: 2813

Group network: This module creates a network with all the friendship connections in the group.

Fifth Network: “ ” group

Page 31: Facebook Networks Analysis

Fifth Network: “ ” group

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• Nodes: 2813

• Edges: 10251

• Directed

• Unweighted

• Avg degree: 7.288

• Graph density: 0.003

• Diameter: 13

• Avg path length: 4.526

• # shortest paths: 4543398

• Modularity: 0.653

• # communities: 666

• Avg clustering coef: 0.287

• Total Triangles: 17515

Fifth Network: “ ” group

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• Degree distribution => scale-free network

Fifth Network: “ ” group

Page 34: Facebook Networks Analysis

• “Quantum Information and Quantum Computer Scientists of the World Unite” group (1945 members): scale-free behaviour

• “Bill Gates” page (10,171,680 likes): scale-free behaviour

• “I fucking love science” page (15,887,023 likes): scale-free behaviour

Other Network that I analyzed

Page 35: Facebook Networks Analysis

References

• “Studying Facebook via data extraction: the Netvizz application”, B.

Rieder, Proceedings of the 5th Annual ACM Web Science Conference,

Pages 346-355, DOI: 10.1145/2464464.2464475

• Netvizz: https://apps.facebook.com/netvizz/

• “Gephi: an open source software for exploring and manipulating networks”, M Bastian, S Heymann, M Jacomy - ICWSM, 2009.

• Gephi: https://gephi.org/