Hands-on activity: Import the example dataset into Gephi and perform the SNA analysis methods

In a previous post, I explored different layouts and performed in details the SNA analysis methods on the blog and Twitter networks for week12 from the Connectivism and Connective Knowledge 2011 course (CCK11) into Gephi.

It’s now time to explore different layouts for the representation of a small network (e.g., Fruchterman Reingold and Yinfan Hu) and experiment with their configuration parameters.

A few weeks ago I posted some measurements commonly seen in social network studies and now let’s play with it!

Layout Algorithms

Figures 1 and 2 show a graph of what might be a small social network.

Fig 1. Applying the Fruchterman Reingold algorithm.

Fig 1. Applying the Fruchterman Reingold algorithm.

 

 

 

 

 

 

 

 

 

 

 

Fig 2. Applying the Yifan Hu algorithm

Fig 2. Applying the Yifan Hu algorithm

 

 

 

 

 

 

 

 

 

 

 

Calculating the network properties Social

SNA draws on concepts from graph theory and structural theory to evaluate network properties such as density, diameter and centralities calculations (Dawson, Tan, and McWilliam, 2011).

  • Diameter : the length of the longest path through the network between any pair of two nodes in the social network.

Diameter = 5.

  • Density: the number of existing connections and the possible connections in the graph.

Density = 0.108.

  • Degree Centrality: the total number of social ties a node has.

From the figures 3, 4 and 5 we can see that Emma, Jill and Shane are the students (nodes) that have the highest number of connections in the network. They have six individual connections. Based on this, they are quite central in most of the potential conversations.

Fig 3. Degree distribution

Fig 3. Degree distribution

 

 

 

 

 

 

 

 

Fig 4. Degree Centrality. Applying nodes size = degree and nodes label = label

Fig 4. Degree Centrality. Applying nodes size = degree and nodes label = label

 

 

 

 

 

 

 

 

 

 

 

 

Fig 5. Degree Centrality. Applying nodes size = degree and nodes label = degree

Fig 5. Degree Centrality. Applying nodes size = degree and nodes label = degree

 

 

 

 

 

 

 

 

 

 

 

 

  • In-degree Centrality: the number of edges coming in. In other words, it indicates popularity or prestige that an individual has in the community. It’s possible to note from Figure 6 the number of other students that are, for example, seeking Jill’s help.
Fig 6. In-degree Centrality. Applying nodes size = In-degree, nodes label = label and nodes color = in-degree

Fig 6. In-degree Centrality. Applying nodes size = In-degree, nodes label = label and nodes color = in-degree

 

 

 

 

 

 

 

 

 

 

 

 

  • Out-degree Centrality: the number of edges leading out. In other words, it indicates gregariousness about an individual. Clearly,  Emma and Bob  influence the greatest number of other students. They have direct influence over 4 students.
Fig 7. Out-degree Centrality. Applying nodes size = Out-degree, nodes label = label and nodes color = out-degree

Fig 7. Out-degree Centrality. Applying nodes size = Out-degree, nodes label = label and nodes color = out-degree

 

 

 

 

 

 

 

 

 

 

 

 

  • Betweenness Centrality: the ease of connection with any other node in the network. If Allen or Liz are removed from network, the entire connection would be completely collapsed with the rest of the community, and so you will notice separated subgroups. It plays an important role which is called “Network Broker”.
Fig 8. Betweenness Centrality. Applying nodes size = betweenness, nodes label = label and nodes color = betweenness

Fig 8. Betweenness Centrality. Applying nodes size = betweenness, nodes label = label and nodes color = betweenness

 

 

 

 

 

 

 

 

 

 

 

 

Network Modularity and Community Identification

It’s a cluster detection algorithm. Students of the same cluster are colored with the same color.

Fig 9. Modularity statistic. Applying nodes color = modularity class

Fig 9. Modularity statistic. Applying nodes color = modularity class

 

 

 

 

 

 

 

 

 

 

 

 

References

Dawson, S., Tan, J. P., McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australasian Journal of Educational Technolog, 27(6), 924-942.

Additional resources

Hirst, T. (2010, April 16). Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part I, Retrieved October 18, 2014, from http://blog.ouseful.info/2010/04/16/getting-started-with-gephi-network-visualisation-app-my-facebook-network-part-i/

Hirst, T. (2010, April 23). Getting Started With Gephi Network Visualisation App – My Facebook Network, Part II: Basic Filters I, Retrieved October 18, 2014, from http://blog.ouseful.info/2010/04/23/getting-started-with-gephi-network-visualisation-app-%E2%80%93-my-facebook-network-part-ii-basic-filters/

Hirst, T. (2010, May 10). Getting Started With Gephi Network Visualisation App – My Facebook Network, Part III: Ego Filters and Simple Network Stats, Retrieved October 18, 2014, from http://blog.ouseful.info/2010/05/10/getting-started-with-gephi-network-visualisation-app-%E2%80%93-my-facebook-network-part-iii-ego-filters-and-simple-network-stats/

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