Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Engineering graph clustering: Models and experimental evaluation
Journal of Experimental Algorithmics (JEA)
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Graph Clustering Via a Discrete Uncoupling Process
SIAM Journal on Matrix Analysis and Applications
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Computer Science Review
An evaluation of community detection algorithms on large-scale email traffic
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
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Graph clustering, the process of discovering groups of similar vertices in a graph, is a very interesting area of study, with applications in many different scenarios. One of the most important aspects of graph clustering is the evaluation of cluster quality, which is important not only to measure the effectiveness of clustering algorithms, but also to give insights on the dynamics of relationships in a given network. Many quality evaluation metrics for graph clustering have been proposed in the literature, but there is no consensus on how do they compare to each other and how well they perform on different kinds of graphs. In this work we study five major graph clustering quality metrics in terms of their formal biases and their behavior when applied to clusters found by four implementations of classic graph clustering algorithms on five large, real world graphs. Our results show that those popular quality metrics have strong biases toward incorrectly awarding good scores to some kinds of clusters, especially seen in larger networks. They also indicate that currently used clustering algorithms and quality metrics do not behave as expected when cluster structures are different from the more traditional, clique-like ones.