Funktionentheorie. 2. Anwendungen und Weiterführung der by Knopp K.

By Knopp K.

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Thus, modularity is the sum over all communities of the difference between the fraction of edges in the same community (eii ) minus the expected value of the fraction of edges (ai2 ) of a network with the same community partition but randomly generated edges. Modularity measures the non-randomness of a graph partition. G; C / C 2˝ (4) The Randomized Greedy Modularity Clustering Algorithm and the Core. . g. Agarwal and Kempe 2008; Brandes et al. 2007). Brandes et al. (2008) have given an integer linear programming formulation for modularity clustering and established that the formal problem is – in the worst case – NP-hard.

Math Soc Sci 4:131–136 Mizuta M, Minami H (2012) Analysis of distribution valued dissimilarity data. In: Gaul WA, Geyer-Schulz A, Schmidt-Thieme L, Kunze J (eds) Challenges at the interface of data analysis, computer science, and optimization. Studies in classification, data analysis, and knowledge organization. Springer, Berlin/Heidelberg, pp 23–28 Pairwise Data Clustering Accompanied by Validation and Visualisation Hans-Joachim Mucha Abstract Pairwise proximities are often the starting point for finding clusters by applying cluster analysis techniques.

In: 10th DIMACS implementation challenge – graph partitioning and graph clustering. pdf Biere A, Heule M, van Maaren H, Walsh T (eds) (2009) Handbook of satisfiability. Frontiers in artificial intelligence and applications, vol 185. IOS, Amsterdam Boguñá M, Pastor-Satorras R, Díaz-Guilera A, Arenas A (2004) Models of social networks based on social distance attachment. Phys Rev E 70(5):056,122 Boldi P, Rosa M, Santini M, Vigna S (2011) Layered label propagation: a multiresolution coordinate-free ordering for compressing social networks.

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