Analysis of Social Networks Using NodeXL
Analyzing Social Networks using NodeXL
Abstract:
Analyzing the social networks by finding page rank, betweenness and closeness centrality, degree, etc through programming requires a lot of coding (time consuming) and graphical representation of such large datasets is a challenge. Generating network statistics and metrics and creating visualizations of network graphs is made easy by using the tool NodeXL provided by the Microsoft in the familiar network of Microsoft Excel as a small add-in. In this part of the project we found the node with the highest pagerank, form communities by k-degree algorithm, reducing the graph size using degree values of each node by exploring the features provided by the NodeXL.
Wiki-Vote Dataset:
This dataset has 1036398 edges and 7115 vertices. The graphical representation of the dataset using NodeXL is:
The visual graph shows that there are a lot of unconnected edges with the other nodes. Our first objective is to reduce the graph. The reduced graph has edges and vertices with degree higher than 2 and in-degree and out-degree for each vertex>=1.This increased the connectivity between the vertices in the graph.
Reducing the graph:
As this is a directed graph, some nodes might not have edges directed to it(in-degree) or directed from it(out-degree).Here in-degree means the number of votes the node received and out-degree is the number he voted for other node. Through NodeXL, we found the nodes with zero in-degree and zero out-degree. This can be done by NodeXL. In the excel sheet NodeXL menu , * Select type of graph as directed from the drop down menu. * Select Graph metrics in the graph menu. * Select in-degree, out-degree, etc in the menu to calculate overall graph metrics which can be used later for future analyzation or just the in-degree and out-degree.
* After calculating in-degree and out-degree, using the excel formula we make the visibility of the nodes with zero in-degree or zero out-degree to
Abstract:
Analyzing the social networks by finding page rank, betweenness and closeness centrality, degree, etc through programming requires a lot of coding (time consuming) and graphical representation of such large datasets is a challenge. Generating network statistics and metrics and creating visualizations of network graphs is made easy by using the tool NodeXL provided by the Microsoft in the familiar network of Microsoft Excel as a small add-in. In this part of the project we found the node with the highest pagerank, form communities by k-degree algorithm, reducing the graph size using degree values of each node by exploring the features provided by the NodeXL.
Wiki-Vote Dataset:
This dataset has 1036398 edges and 7115 vertices. The graphical representation of the dataset using NodeXL is:
The visual graph shows that there are a lot of unconnected edges with the other nodes. Our first objective is to reduce the graph. The reduced graph has edges and vertices with degree higher than 2 and in-degree and out-degree for each vertex>=1.This increased the connectivity between the vertices in the graph.
Reducing the graph:
As this is a directed graph, some nodes might not have edges directed to it(in-degree) or directed from it(out-degree).Here in-degree means the number of votes the node received and out-degree is the number he voted for other node. Through NodeXL, we found the nodes with zero in-degree and zero out-degree. This can be done by NodeXL. In the excel sheet NodeXL menu , * Select type of graph as directed from the drop down menu. * Select Graph metrics in the graph menu. * Select in-degree, out-degree, etc in the menu to calculate overall graph metrics which can be used later for future analyzation or just the in-degree and out-degree.
* After calculating in-degree and out-degree, using the excel formula we make the visibility of the nodes with zero in-degree or zero out-degree to