Degree Centrality

This article is part of BD517 Social and Information Network Analysis, Big Data Engineering Program, Master of Engineering, CITE, DPU.

Social networks is a website that brings people together to talk, share ideas and interests, or make new friends. This type of collaboration and sharing is known as social media. Unlike traditional media that is created by no more than ten people, social media sites contain content created by hundreds or even millions of different people. A small list of some of the biggest social networks used today such as facebook, Instagram, LinkedIn, Twitter, Youtube etc. To analyze these networks we can use Social Network Analysis (SNA). These is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them.

For this article would like to measures by centrality measure.These algorithms use graph theory to calculate the importance of any given node in a network . Vital tool for understanding the behavior of networks and graphs with R programming.

Data set : wiki-Vote (Social Networks)

Download network data from The Network Data Repository with Interactive Graph Analytics and Visualization

This network dataset is in the category of Social Networks Visualize soc-wiki-Vote’s link structure and discover valuable insights using the interactive network data visualization and analytics platform. Compare with hundreds of other network data sets across many different categories and domains.

Description : The dataset contains all the Wikipedia voting data from the inception of Wikipedia till January 2008. Nodes in the network represent wikipedia users and a directed edge from node i to node j represents that user i voted on user j.

Building the Network

How to convert this dataset into a network object in R?

There are multiple packages to work with networks, but the most popular is igraph because it’s very flexible and easy to do, and it’s much faster and scales well to very large networks. Other packages that you may want to explore are readr, haven, ggplot2, and philentropy.

Create an iGraph Style Edgelist

Layout options : set seed to make the layout reproducible

Removing Self-Loops (Repondents Nominating Themselves)

Node or Vetex Options: Size and Color
Edge Options: Color

Plotting, Now Specifying an arrow size and getting rid of arrow heads. I am letting the color and the size of the node indicate the directed nature of the graph.

Centrality algorithms are used to find the most influential nodes in a graph. Many of these algorithms were invented in the field of social network analysis.

Definition: Degree centrality assigns an importance score based purely on the number of links held by each node.

What it tells us: How many direct, ‘one hop’ connections each node has to other nodes within the network.

When to use it: For finding very connected individuals, popular individuals, individuals who are likely to hold most information or individuals who can quickly connect with the wider network.

A bit more detail: Degree centrality is the simplest measure of node connectivity. Sometimes it’s useful to look at in-degree (number of inbound links) and out-degree (number of outbound links) as distinct measures, for example when looking at transactional data or account activity.

Degree: In, Out, All Centrality

Definition: Betweenness centrality measures the number of times a node lies on the shortest path between other nodes.

What it tells us: This measure shows which nodes act as ‘bridges’ between nodes in a network. It does this by identifying all the shortest paths and then counting how many times each node falls on one.

When to use it: For finding the individuals who influence the flow around a system.

A bit more detail: Betweenness is useful for analyzing communication dynamics, but should be used with care. A high betweenness count could indicate someone holds authority over, or controls collaboration between, disparate clusters in a network; or indicate they are on the periphery of both clusters.

Definition: This measure scores each node based on their ‘closeness’ to all other nodes within the network.

What it tells us: This measure calculates the shortest paths between all nodes, then assigns each node a score based on its sum of shortest paths.

When to use it: For finding the individuals who are best placed to influence the entire network most quickly.

A bit more detail: Closeness centrality can help find good ‘broadcasters’, but in a highly connected network you will often find all nodes have a similar score. What may be more useful is using Closeness to find influencers within a single cluster.

References :

Eakasit Pacharawongsakda, Ph.D., BD517 Social and Information Network Analysis Course

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