Monday, December 15, 2014

Competency 3.1 - Basics of Social Network Analysis

I'm going back to Weeks 3 and 4 to learn about Social Network Analysis since the course is nearing completion. I will go back to the final wrap up Week 9 after I finish these two weeks' lessons.

Competency 3.1: Define social network analysis and its main analysis methods.

Social Network Analysis (SNA) provides insights into how different social processes unfold while learning happens in any learning environment. It helps us to study the effects of interaction and social context in education. The different network elements are actors and their relations. 

The nodes/ actors could be students email addresses, tweets or any such actions. I would typically use SNA to see the interaction between students, for example in a chatroom/ discussion forum, to see who is talking to whom, who replies to whom, who is following what question, who voted for a question etc. Based on the interaction patterns, we can construct the network graph. We can from here see if any measure from the network can correlate to learning or performance.  

Some measures in SNA for analysis are below:


Diameter determines the longest distance between any pair of nodes in a network. It measures the extent to which each individual node can communicate with any other node in the network. 


Density determines the potential of the entire network to talk to each other. It can be used to determine the extent to which some individual nodes share the information. The spread of information is very fast in a highly dense network. 

Degree Centrality:

Degree centrality is a simple measure that indicates the overall number of connections for each actor in a network. Network measures may have specific meaning when considered in the context of directed graphs.
In-Degree Centrality:
In-degree centrality is a measure of the number of other nodes that directly try to establish connection to a particular node. Also refers to the popularity or prestige of a node in a network.
Out-Degree Centrality:
Out-degree centrality is the measure of the number of nodes to which particular nodes are talking. 

Betweenness Centrality:

Betweenness centrality indicates the ease of connection with anybody else in the network, in particular, to try to connect all small sub communities in the network. Brokerage role is best measured by this measure.

Closeness Centrality:

Closeness centrality measures the ease or the shortest distance of a node to anybody else in the network. It indicates how quickly a node can get to another node in the network.

Network Modularity:

Network modularity is used to identify common sub-groups talking to each other where a group of actors have close ties to each other. An algorithm for finding the giant component can be used to identify the largest component of all connected nodes in the network. This filters out single nodes that are not connected to the network to easily identify and analyse communities in the network.

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All materials are based on the EdX course - Data, Analytics and Learning
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