Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Exploratory Social Network Analysis with Pajek
Exploratory Social Network Analysis with Pajek
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Exploring local community structures in large networks
Web Intelligence and Agent Systems
Detecting Communities in Large Networks by Iterative Local Expansion
CASON '09 Proceedings of the 2009 International Conference on Computational Aspects of Social Networks
Relative clustering validity criteria: A comparative overview
Statistical Analysis and Data Mining
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Entropy-Based Graph Clustering: Application to Biological and Social Networks
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
A Diffusion of Innovation-Based Closeness Measure for Network Associations
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Evolving clusters in gene-expression data
Information Sciences: an International Journal
On the combination of relative clustering validity criteria
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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Grouping data points is one of the fundamental tasks in data mining, which is commonly known as clustering if data points are described by attributes. When dealing with interrelated data that does not have any attributes and is represented in the form of nodes and their relationships, this task is also referred to as community mining. There has been a considerable number of approaches proposed in recent years for mining communities in a given network. But little work has been done on how to evaluate community mining results. The common practice is to use an agreement measure to compare the mining result against a ground truth, however, the ground truth is not known in most of the real world applications. In this paper, we investigate relative clustering quality measures defined for evaluation of clustering data points with attributes and propose proper adaptations to make them applicable in the context of social networks. Not only these relative criteria could be used as metrics for evaluating quality of the groupings but also they could be used as objectives for designing new community mining algorithms.