Analysis of Asymmetric Communication Patterns in Computer by Bettina Hoser

By Bettina Hoser

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6)). 13 ❧♦ ❦ ❤❜ ❝ ❣❥ ♦ ❜❝ ❣❱❤ ❧♥ ❦ ❤❜ ❝ ❣❥ ♥ ❜❝ ❞ ❣❱❤ ❛❜ ❝ ❧ ♠ ❜❦ ❝ ❤ ❣❥ ♠ ❜❝ ❣❤ ❧✐ ❦ ❤ ❜❳❝ ❣❥ ✐ ❜❝ ❣❤ 1 2 3 4 52 CHAPTER 4 CHAPTER 4 53 The eigensystem also indicates the location of the perturbing event.

Thus the effi of two ❢ vertices regarding information transfer is presumed to ciency ⑨ ☎✞✝ ✛ be proportional to ⑩ ❜❁❶❸❷ ☎q✇ ✝❡❿ . The efficiency of the network is then the aver✌ ✇ ➆q➇➉➈✞➊➌➋ ❶ ❫ ❫❺❹❼❻❾❽ age ➀✖❅➂➁➄❜ ➃✞➅ ❜ ❢✸❿ ➃ . 7) adds an exante status, given as a vector ➏ to the centrality measure and weighs the relative importance of the grouped-derived rank in comparison to the member inherent status by a coefficient ❑ . 91]. A vertex ✚ is central if it has minimal excentricity. ✒ Excentricity ✜ ✪ being defined as ✜ ✪➓☎ ❅→➔ ✄✆✪➓✝ ✷✆●r❴❵st✵✸✰ ✱❲✹ .

5) ✵❲▼❧❦⑤✲✧✹t✵❲▼⑥❦⑦❊⑧✹ with ② ☎✞✝ the number of paths between vertex ✰ and ✱ and ② ☎✞❫ ✝ the number of paths between vertex ✰ and ✱ that contain vertex ● . This view requires the assumption that the choice of communication channel/path is equally distributed. That might not be the case, if preferential behavior is taken into account. Betweenness centrality has been used to detect clusters within networks [GN02], [NG03]. ) Information centrality is a stronger version of betweenness centrality, in that it now takes into account that paths between two vertices might not be taken for the transport of information on an equally distributed basis.

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