Network centrality measures
1. Network centrality measures
Network centrality measures are critical to identifying key players and influencers from a network.2. Lesson overview
In this lesson, we understand the concepts of network centrality measures and the two key centrality measures, degree centrality and betweenness. We will calculate these measures for a retweet network to identify the key players in the network and the role they can play in a promotional campaign.3. Network centrality measures
The influence of a vertex is determined by its number of edges and position within the network. Network centrality is the measure of importance of a vertex in a network. Network centrality measures assign a numerical value to each vertex according to its influence on other vertices. We will be focusing on the measures degree centrality and betweenness centrality in this lesson.4. Degree centrality
Degree centrality is the simplest measure of vertex influence and it determines how many edges or connections a vertex has. In a directed network, vertices will have out-degree and in-degree scores.5. Out-degree
The out-degree represents how many outgoing edges a vertex has. In a retweet network, the out-degree of a vertex indicates the number of times a user retweets posts.6. In-degree
The in-degree represents how many incoming edges each vertex has. In a retweet network, in-degree indicates the number of times the user's posts are retweeted.7. Degree centrality of a user
The out-degree and in-degree centrality of a user in a retweet network can be calculated using the function degree() from the igraph package. This function takes the following arguments: The retweet network, the user, and mode set to “out” to extract the out-degree. The value of 20 for out-degree indicates that the user has retweeted 20 times on the topic. To calculate the in-degree, we use the same function and arguments but set the mode to “in”. The value of 23 for in-degree indicates that this user’s posts have been retweeted 23 times.8. Users who retweeted most
Let us identify users who retweet the most by calculating out-degree for the retweet network. To find the top 3 users who retweet the most, we sort the array in descending order of the out-degree using the sort() function.9. Users who retweeted most
The top 3 users and their out-degree values are displayed here. These users are key players who can be used as a medium to retweet promotional posts of a fashion brand.10. Users whose posts were retweeted most
We will now calculate in-degree scores for the network to identify users whose posts were retweeted the most. The degree() and sort() functions are used again to calculate the in-degree values and sort the users based on the in-degree values.11. Users whose posts were retweeted most
Here, the users with the top 3 in-degrees are influential as their tweets are retweeted many times. They can be used to initiate branding messages of a fashion brand.12. Betweenness
Betweenness represents the degree to which nodes stand between each other. This measure captures a user's role in allowing information to pass from one part of the network to the other. A node with higher betweenness centrality has more control over the network because more information will pass through that node.13. Identifying users with high betweenness
We can identify the top users based on betweenness scores using the betweenness() function. The function takes two arguments: the retweet network and the value TRUE for directed. We sort the array in descending order of betweenness scores and view the top 3 users.14. Identifying users with high betweenness
These users are key bridges between people who retweet frequently and users whose tweets are retweeted frequently. They are important to the flow of information through the retweet network.15. Let's practice!
Let's practice by identifying influential users in a retweet network on travel.Create Your Free Account
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