Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
Visual analysis of network centralities
APVis '06 Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation - Volume 60
NodeTrix: a Hybrid Visualization of Social Networks
IEEE Transactions on Visualization and Computer Graphics
Locating Key Actors in Social Networks Using Bayes' Posterior Probability Framework
EuroISI '08 Proceedings of the 1st European Conference on Intelligence and Security Informatics
Social Network Discovery Based on Sensitivity Analysis
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
How Much Similar Are Terrorists Networks of Istanbul?
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
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This paper introduces a technique to analyze and visualize social networks using Shannon's entropy model. Entropy is exploited to measure the information amount in social network graphs, and to conduct sensitivity analyses. Novel measures such as degree, betweenness and closeness entropies are evaluated to find the change in graph entropy or the actors. In this work we present a visualization approach that uses coloring, sizing and filtering to help the users perceive the communicated information. The result of sensitivity analyses is integrated into the visualization using the change amount caused by the actors as information. The main contribution of this study is a visualization where the information communicated from a social network is enhanced by the help of sensitivity analyses.