Elements of information theory
Elements of information theory
Software Components Capture Using Graph Clustering
IWPC '03 Proceedings of the 11th IEEE International Workshop on Program Comprehension
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
On the Visualization of Social and other Scale-Free Networks
IEEE Transactions on Visualization and Computer Graphics
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Inferring relevant social networks from interpersonal communication
Proceedings of the 19th international conference on World wide web
The Journal of Machine Learning Research
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
PIKM 2013: the 6th ACM workshop for ph.d. students in information and knowledge management
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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A network represented as a graph, can be transformed to a sparser graph, if a threshold is applied to the relationship between its objects. The threshold can be used as an upper or lower limit or define a range based on which we can exclude connections from the graph, thus resulting to different views of a graph. We examine for various values of the threshold the effect it has on the task of community detection and we propose a method to validate the results of the corresponding clusterings against the clustering of the original graph. We transform the clusterings in comparable forms and we employ four known measures for clustering validation in order to examine their resemblance. We present some preliminary experiments to evaluate the effects of a threshold on the clustering task and we outline possible usage of the different views that are produced.