Abstract
Modularity, the recently defined quality measure for clusterings, has attained instant popularity in the fields of social and natural sciences. We revisit the rationale behind the definition of modularity and explore the founding paradigm. This paradigm is based on the trade-off between the achieved quality and the expected quality of a clustering with respect to networks with similar intrinsic structure. We experimentally evaluate realizations of this paradigm systematically, including modularity, and describe efficient algorithms for their optimization. We confirm the feasibility of the resulting generality by a first systematic analysis of the behavior of these realizations on both artificial and on real-world data, arriving at remarkably good results of community detection.
This work was partially supported by the DFG under grants WA 654/14-3 and by EU under grant DELIS (contract no. 001907) and CREEN project (contract no. 012684).
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Gaertler, M., Görke, R., Wagner, D. (2007). Significance-Driven Graph Clustering. In: Kao, MY., Li, XY. (eds) Algorithmic Aspects in Information and Management. AAIM 2007. Lecture Notes in Computer Science, vol 4508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72870-2_2
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DOI: https://doi.org/10.1007/978-3-540-72870-2_2
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