Social Network Analytics

Social Network Analytics

Social Network Analytics

Contents

1. Introduction 2

2. Basic Topological Properties 2

2.1. General Information 2

2.2. Modularity 2

2.3. Location of Nodes 3

2.4. Diameter, Average path length, Number of shortest paths 3

3. Component measures 4

3.1 Giant components 4

3.2 Weakly connected component 4

3.3. Component size distribution 5

4. Degree measures 5

4.1. Maximum Degree 5

4.2. Average Degree and Degree Distribution 6

5. Centrality measures 7

5.1. Betweenness Centrality 7

5.2. Closeness Centrality 8

5.3. Eigenvector Centrality 9

6. Clustering effects 10

6.1. Clustering Coefficient 10

6.2. Number of triangles 11

6.3. Clustering coefficient distribution 11

7. Gender and Homophily 11

7.1. Gender 11

7.2. Homophily 11

1. Introduction

The last decade there was a rapid growth of online networks (such as Facebook, Twitter, Linkedin, Google +, e.t.c.). Everyone is connected with someone else and all of us we are part of our “small world”.

This growth is the result of a new field of sciences, the Social Network Analysis (SNA). SNA is the mapping of relationships and flows between people, groups, organizations or other entities, using graphs (the nodes in the network are the people and groups while the links show relationships or flows between the nodes).

Objective of this report is to analyze my Facebook profile, using as a tool Gephi, illustrating the basic topological properties and measuring a number of components.

2. Basic Topological Properties

2.1. General Information

My Facebook profile consists of 389 nodes. They are connected to each other by 5.256 edges.

Figure 1. Nodes and Edges

2.2. Modularity

Modularity is one basic measure of the structure of the network. It is designed to measure the strength of division of a network into modules (also called groups, clusters or communities).

My network is divided into 23 sub-groups. The...