Engineering Comparators for Graph Clusterings

A promising approach to compare two graph clusterings is based on using measurements for calculating the distance between them. Existing measures either use the structure of clusterings or quality-based aspects with respect to some index evaluating both clusterings. Each approach suffers from conceptional drawbacks. We introduce a new approach combining both aspects and leading to better results for comparing graph clusterings. An experimental evaluation of existing and new measures shows that the significant drawbacks of existing techniques are not only theoretical in nature but manifest frequently on different types of graphs. The evaluation also proves that the results of our new measures are highly coherent with intuition, while avoiding the former weaknesses.

[1]  GhoshJoydeep,et al.  Cluster ensembles --- a knowledge reuse framework for combining multiple partitions , 2003 .

[2]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[3]  Marina Meila,et al.  Comparing clusterings: an axiomatic view , 2005, ICML.

[4]  Marina Meila,et al.  Comparing Clusterings by the Variation of Information , 2003, COLT.

[5]  S. vanDongen.  Performance criteria for graph clustering and Markov cluster experiments , 2000 .

[6]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[7]  Marc Vidal,et al.  Interactome modeling , 2005, FEBS letters.

[8]  Dorothea Wagner,et al.  Generating Significant Graph Clusterings , 2006 .

[9]  M. V. Valkenburg.  Network Analysis , 1964 .

[10]  S. Dongen.  A cluster algorithm for graphs , 2000 .

[11]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[12]  Alan Agresti,et al.  The Measurement of Classification Agreement: An Adjustment to the Rand Statistic for Chance Agreement , 1984 .

[13]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[14]  Dorothea Wagner,et al.  Significance-Driven Graph Clustering , 2007, AAIM.

[15]  Ulrik Brandes,et al.  Experiments on Graph Clustering Algorithms , 2003, ESA.

[16]  Ana L. N. Fred,et al.  Robust data clustering , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[17]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Costas S. Iliopoulos,et al.  A New Efficient Algorithm for Computing the Longest Common Subsequence , 2007, AAIM.

[19]  Andrzej Pelc,et al.  Deterministic Rendezvous in Graphs , 2003 .

[20]  Silke Wagner,et al.  Comparing Clusterings - An Overview , 2007 .