Closeness Centralization Measure for Two-mode Data of Prescribed Sizes

Abstract : We confirm a conjecture by Everett, Sinclair, and Dankelmann [Some Centrality results new and old, J. Math. Sociology 28 (2004), 215–227] regarding the problem of maximizing closeness centralization in two-mode data, where the number of data of each type is fixed. Intuitively, our result states that among all networks obtainable via two-mode data, the largest closeness is achieved by simply locally maximizing the closeness of a node. Mathematically, our study concerns bipartite graphs with fixed size bipartitions, and we show that the extremal configuration is a rooted tree of depth 2, where neighbors of the root have an equal or almost equal number of children.
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  • HAL Id : hal-01289470, version 1


Matjaž Krnc, Jean-Sébastien Sereni, Ristě Skrekovski, Zelealem Yilma. Closeness Centralization Measure for Two-mode Data of Prescribed Sizes. Network Science, 2016. 〈hal-01289470〉



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