Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Bergamini, Elisabetta"'
Autor:
van der Grinten, Alexander, Bergamini, Elisabetta, Green, Oded, Bader, David A., Meyerhenke, Henning
Network analysis defines a number of centrality measures to identify the most central nodes in a network. Fast computation of those measures is a major challenge in algorithmic network analysis. Aside from closeness and betweenness, Katz centrality i
Externí odkaz:
http://arxiv.org/abs/1807.03847
Closeness is a widely-studied centrality measure. Since it requires all pairwise distances, computing closeness for all nodes is infeasible for large real-world networks. However, for many applications, it is only necessary to find the k most central
Externí odkaz:
http://arxiv.org/abs/1710.01143
Closeness is a widely-used centrality measure in social network analysis. For a node it indicates the reciprocal of the average shortest-path distance to the other nodes of the network. While the identification of the k nodes with highest closeness r
Externí odkaz:
http://arxiv.org/abs/1710.01144
Finding central nodes is a fundamental problem in network analysis. Betweenness centrality is a well-known measure which quantifies the importance of a node based on the fraction of shortest paths going though it. Due to the dynamic nature of many to
Externí odkaz:
http://arxiv.org/abs/1704.08592
Autor:
Bergamini, Elisabetta, Borassi, Michele, Crescenzi, Pierluigi, Marino, Andrea, Meyerhenke, Henning
Given a connected graph $G=(V,E)$, the closeness centrality of a vertex $v$ is defined as $\frac{n-1}{\sum_{w \in V} d(v,w)}$. This measure is widely used in the analysis of real-world complex networks, and the problem of selecting the $k$ most centr
Externí odkaz:
http://arxiv.org/abs/1704.01077
Autor:
Bergamini, Elisabetta, Crescenzi, Pierluigi, D'Angelo, Gianlorenzo, Meyerhenke, Henning, Severini, Lorenzo, Velaj, Yllka
Betweenness is a well-known centrality measure that ranks the nodes according to their participation in the shortest paths of a network. In several scenarios, having a high betweenness can have a positive impact on the node itself. Hence, in this pap
Externí odkaz:
http://arxiv.org/abs/1702.05284
Matrices associated with graphs, such as the Laplacian, lead to numerous interesting graph problems expressed as linear systems. One field where Laplacian linear systems play a role is network analysis, e. g. for certain centrality measures that indi
Externí odkaz:
http://arxiv.org/abs/1607.02955
Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths. Since an exact computation is prohibitive in large networks, several approximation algorithms have been proposed. Bes
Externí odkaz:
http://arxiv.org/abs/1510.07971
Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths. Since an exact computation is prohibitive in large networks, several approximation algorithms have been proposed. Bes
Externí odkaz:
http://arxiv.org/abs/1504.07091
Betweenness centrality ranks the importance of nodes by their participation in all shortest paths of the network. Therefore computing exact betweenness values is impractical in large networks. For static networks, approximation based on randomly samp
Externí odkaz:
http://arxiv.org/abs/1409.6241