Ranking of Closeness Centrality for Large-Scale Social Networks

By Kazuya Okamoto, Xiangyang Li. Frontiers in Algorithmics, 2008

419 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/66f830d4940c391cfaff202c5408180d28e5c92b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Julia Heidemann
2. Konstantinos Skianis
3. Bettina Friedl
4. Andrea Landherr
5. Fragkiskos D. Malliaros

In Statistical and Nonlinear Physics

1. Julia Heidemann
2. Konstantinos Skianis
3. Bettina Friedl
4. Andrea Landherr
5. Fragkiskos D. Malliaros

Citations in the corpus

• A Critical Review of Centrality Measures in Social Networks (2010, positive): “1 At the same time, this algorithm allows the calculation of other distance-based CM, such as CC, for which Okamoto et al. (2008) discuss other algorithms and heuristics.”
• Graph-based term weighting for text categorization (2015, background): “The closeness centrality of a node i is defined as the inverse of the average shortest path distance from the node to any other node in the graph [27]:”