NDLI: Algorithms and Experiments for Betweeness Centrality in Tree-Like Networks Bachelorarbeit

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Algorithms and Experiments for Betweeness Centrality in Tree-Like Networks Bachelorarbeit

Content Provider	Semantic Scholar
Author	Walsh, Toby
Copyright Year	2017
Abstract	When it comes to analyzing networks the betweenness centrality is a standard measure to rank the importance of the vertices in a network. The betweenness centrality represents the relative number of shortest paths a vertex lies on in a network. Finding vertices with a high betweenness centrality and thus vertices that are important for the network, has several applications in different fields of science, for example in sociology, biology and computer science. The currently best known algorithms for computing the betweenness centrality for each vertex of a network have a running time of O(n · m), where n is the number of vertices and m is the number of edges in the network. This running time however is not feasible for very big instances of networks. This motivates to find faster algorithms or to improve existing algorithms to make the betweenness centrality a usable method of analysis even for these instances which are currently to big for practical use. One such O(n · m)-algorithm is the one of Brandes’ [J Math Sociol 2001]. Baglioni et al. [ASONAM 2012] improved Brandes’ algorithm by deleting degree-one vertices in the network before running Brandes’ algorithm on the network. If enough vertices are deleted, then this can be a significant speed up. In this work we follow this idea. In particular we provide a parameterized algorithm with respect to the feedback edge number, which involves the deletion of some vertices of degree two.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://fpt.akt.tu-berlin.de/publications/theses/BA-Alexander-Dittmann.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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