. . . . . "\u5728\u56FE\u8BBA\u4E2D\uFF0C\u4ECB\u6570\u4E2D\u5FC3\u6027\uFF08\u82F1\u8A9E\uFF1ABetweenness Centrality\uFF09\u662F\u57FA\u4E8E\u6700\u77ED\u8DEF\u5F84\u9488\u5BF9\u7F51\u7EDC\u56FE\u7684\u8861\u91CF\u6807\u51C6\u4E4B\u4E00\u3002\u9488\u5BF9\u5168\u8FDE\u63A5\u7F51\u7EDC\u56FE\uFF0C\u5176\u4E2D\u4EFB\u610F\u4E24\u4E2A\u8282\u70B9\u5747\u81F3\u5C11\u5B58\u5728\u4E00\u4E2A\u6700\u77ED\u8DEF\u5F84\uFF0C\u5728\u65E0\u6743\u91CD\u7F51\u7EDC\u56FE\u4E2D\u8BE5\u6700\u77ED\u8DEF\u5F84\u662F\u8DEF\u5F84\u5305\u542B\u8FB9\u7684\u6570\u91CF\u6C42\u548C\uFF0C\u52A0\u6743\u7F51\u7EDC\u56FE\u4E2D\u8BE5\u6700\u77ED\u8DEF\u5F84\u5219\u662F\u8DEF\u5F84\u5305\u542B\u8FB9\u7684\u6743\u91CD\u6C42\u548C\u3002\u6BCF\u4E2A\u8282\u70B9\u7684\u4ECB\u6570\u4E2D\u5FC3\u6027\u5373\u4E3A\u8FD9\u4E9B\u6700\u77ED\u8DEF\u5F84\u7A7F\u8FC7\u8BE5\u8282\u70B9\u7684\u6B21\u6570\u3002 \u4ECB\u6570\u4E2D\u5FC3\u6027\u5728\u7F51\u7EDC\u7406\u8BBA\u4E2D\u6709\u5E7F\u6CDB\u7684\u5E94\u7528\uFF1A\u5B83\u4EE3\u8868\u4E86\u67D0\u8282\u70B9\u4E0E\u5176\u4ED6\u8282\u70B9\u4E4B\u95F4\u7684\u4E92\u52A8\u7A0B\u5EA6\u3002 \u4F8B\u5982\uFF0C\u5728\u4E2D\uFF0C\u4E00\u4E2A\u6709\u66F4\u9AD8\u4ECB\u6570\u4E2D\u5FC3\u6027\u7684\u8282\u70B9\u5728\u7F51\u7EDC\u4E2D\u6709\u66F4\u5F3A\u7684\u63A7\u5236\u80FD\u529B\uFF0C\u56E0\u4E3A\u66F4\u591A\u7684\u4FE1\u606F\u4F20\u9012\u65F6\u5C06\u901A\u8FC7\u8BE5\u8282\u70B9\u3002 \u4ECB\u6570\u4E2D\u5FC3\u6027\u88AB\u7528\u4F5C\u4E3A\u5BF9\u4E2D\u5FC3\u6027\u7684\u4E00\u79CD\u5E38\u89C1\u6D4B\u91CF\u65B9\u5F0F: \u5B83\u9002\u7528\u4E8E\u89E3\u51B3\u7F51\u7EDC\u7406\u8BBA\u4E2D\u7684\u8BB8\u591A\u95EE\u9898\uFF0C\u5305\u62EC\u4E0E\u793E\u4F1A\u7F51\u7EDC\u3001\u751F\u7269\u3001\u8FD0\u8F93\u548C\u79D1\u5B66\u5408\u4F5C\u7B49\u65B9\u9762\u76F8\u5173\u7684\u95EE\u9898\u3002 \u867D\u7136\u65E9\u671F\u7684\u7814\u7A76\u4EBA\u5458\u66FE\u76F4\u89C2\u5730\u63CF\u8FF0\u4E86\u4ECB\u6570\u7684\u4E2D\u5FC3\u6027\uFF0C\u4F46\u57281977\u5E74\u7ED9\u4E86\u7B2C\u4E00\u4E2A\u4ECB\u6570\u4E2D\u5FC3\u6027\u7684\u6B63\u5F0F\u5B9A\u4E49\u3002"@zh . "\u4ECB\u6570\u4E2D\u5FC3\u6027"@zh . "A intermedia\u00E7\u00E3o \u00E9 uma medida de centralidade de um n\u00F3 em uma rede. Ela \u00E9 igual ao n\u00FAmero de menores caminhos de todos os v\u00E9rtices para quaisquer outros v\u00E9rtices que passam por aquele n\u00F3. A intermedia\u00E7\u00E3o \u00E9 uma medida mais \u00FAtil do que apenas a conectividade de um n\u00F3. A primeira \u00E9 mais global para a rede, enquanto a segunda tem apenas um efeito local. O desenvolvimento da intermedia\u00E7\u00E3o \u00E9 geralmente atribu\u00EDdo ao soci\u00F3logo Linton Freeman, que tamb\u00E9m desenvolveu v\u00E1rias outras medidas de centralidade. A mesma ideia tamb\u00E9m foi proposta pelo matem\u00E1tico J. Anthonisse, embora seu trabalho nunca tenha sido publicado."@pt . . . . . . . "31605745"^^ . . "1105974046"^^ . "Centralidad de intermediaci\u00F3n"@es . . . . . . "Centralit\u00E9 interm\u00E9diaire"@fr . "\u0421\u0442\u0435\u043F\u0435\u043D\u044C \u043F\u043E\u0441\u0440\u0435\u0434\u043D\u0438\u0447\u0435\u0441\u0442\u0432\u0430"@ru . . . "En th\u00E9orie des graphes et th\u00E9orie des r\u00E9seaux, la centralit\u00E9 interm\u00E9diaire, centralit\u00E9 d'interm\u00E9diarit\u00E9 ou interm\u00E9diarit\u00E9 est une mesure de centralit\u00E9 d'un sommet d'un graphe. Elle est \u00E9gale au nombre de fois que ce sommet est sur le chemin le plus court entre deux autres n\u0153uds quelconques du graphe. Un n\u0153ud poss\u00E8de une grande interm\u00E9diarit\u00E9 s'il a une grande influence sur les transferts de donn\u00E9es dans le r\u00E9seau, sous l'hypoth\u00E8se que ces transferts se font uniquement par les chemins les plus courts."@fr . . . . . "18688"^^ . . . "Betweenness centrality"@en . . . . 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"En an\u00E1lisis de redes sociales, la centralidad de intermediaci\u00F3n, o simplemente intermediaci\u00F3n (en ingl\u00E9s, betweenness) es una medida de centralidad que cuantifica la frecuencia o el n\u00FAmero de veces que un nodo se encuentra entre las geod\u00E9sicas o caminos m\u00E1s cortos de otros actores. Un actor tendr\u00E1 una alta intermediaci\u00F3n si es un v\u00E9rtice de corte para muchas geod\u00E9sicas entre actores.\u200B\u200B Para , esta medida permite cuantificar el control de un humano en la comunicaci\u00F3n existente con otros humanos en una red social. La idea intuitiva es que si se eligen dos nodos al azar, y luego tambi\u00E9n al azar uno de los eventuales posibles caminos m\u00E1s cortos entre ellos, entonces los nodos con mayor intermediaci\u00F3n ser\u00E1n aquellos que aparezcan con mayor probabilidad dentro de este camino.\u200B"@es . "Intermedia\u00E7\u00E3o"@pt . "\u0421\u0442\u0435\u043F\u0435\u043D\u044C \u043F\u043E\u0441\u0440\u0435\u0434\u043D\u0438\u0447\u0435\u0441\u0442\u0432\u0430 \u2014 \u044D\u0442\u043E \u043C\u0435\u0440\u0430 \u0446\u0435\u043D\u0442\u0440\u0430\u043B\u044C\u043D\u043E\u0441\u0442\u0438 \u0432 \u0433\u0440\u0430\u0444\u0435, \u043E\u0441\u043D\u043E\u0432\u0430\u043D\u043D\u0430\u044F \u043D\u0430 \u043A\u0440\u0430\u0442\u0447\u0430\u0439\u0448\u0438\u0445 \u043F\u0443\u0442\u044F\u0445. \u0414\u043B\u044F \u043B\u044E\u0431\u043E\u0439 \u043F\u0430\u0440\u044B \u0432\u0435\u0440\u0448\u0438\u043D \u0432 \u0441\u0432\u044F\u0437\u043D\u043E\u043C \u0433\u0440\u0430\u0444\u0435 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0443\u0435\u0442 \u043F\u043E \u043C\u0435\u043D\u044C\u0448\u0435\u0439 \u043C\u0435\u0440\u0435 \u043E\u0434\u0438\u043D (\u043A\u0440\u0430\u0442\u0447\u0430\u0439\u0448\u0438\u0439) \u043F\u0443\u0442\u044C \u043C\u0435\u0436\u0434\u0443 \u0432\u0435\u0440\u0448\u0438\u043D\u0430\u043C\u0438, \u0434\u043B\u044F \u043A\u043E\u0442\u043E\u0440\u043E\u0433\u043E \u043C\u0438\u043D\u0438\u043C\u0430\u043B\u044C\u043D\u043E \u043B\u0438\u0431\u043E \u0447\u0438\u0441\u043B\u043E \u0440\u0451\u0431\u0435\u0440, \u043F\u043E \u043A\u043E\u0442\u043E\u0440\u044B\u043C \u043F\u0443\u0442\u044C \u043F\u0440\u043E\u0445\u043E\u0434\u0438\u0442, (\u0434\u043B\u044F \u043D\u0435\u0432\u0437\u0432\u0435\u0448\u0435\u043D\u043D\u044B\u0445 \u0433\u0440\u0430\u0444\u043E\u0432), \u043B\u0438\u0431\u043E \u0441\u0443\u043C\u043C\u0430 \u0432\u0435\u0441\u043E\u0432 \u044D\u0442\u0438\u0445 \u0440\u0451\u0431\u0435\u0440 (\u0434\u043B\u044F \u0432\u0437\u0432\u0435\u0448\u0435\u043D\u043D\u044B\u0445 \u0433\u0440\u0430\u0444\u043E\u0432). \u0421\u0442\u0435\u043F\u0435\u043D\u044C \u043F\u043E\u0441\u0440\u0435\u0434\u043D\u0438\u0447\u0435\u0441\u0442\u0432\u0430 \u0434\u043B\u044F \u043A\u0430\u0436\u0434\u043E\u0439 \u0432\u0435\u0440\u0448\u0438\u043D\u044B \u0440\u0430\u0432\u043D\u0430 \u0447\u0438\u0441\u043B\u0443 \u044D\u0442\u0438\u0445 \u043A\u0440\u0430\u0442\u0447\u0430\u0439\u0448\u0438\u0445 \u043F\u0443\u0442\u0435\u0439 \u0447\u0435\u0440\u0435\u0437 \u0432\u0435\u0440\u0448\u0438\u043D\u0443. \u0421\u0442\u0435\u043F\u0435\u043D\u044C \u043F\u043E\u0441\u0440\u0435\u0434\u043D\u0438\u0447\u0435\u0441\u0442\u0432\u0430 \u043D\u0430\u0445\u043E\u0434\u0438\u0442 \u0448\u0438\u0440\u043E\u043A\u043E\u0435 \u043F\u0440\u0438\u043C\u0435\u043D\u0435\u043D\u0438\u0435 \u0432 \u2014 \u043E\u043D\u0430 \u043E\u0442\u0440\u0430\u0436\u0430\u0435\u0442 \u0441\u0442\u0435\u043F\u0435\u043D\u044C, \u0432 \u043A\u043E\u0442\u043E\u0440\u043E\u0439 \u0432\u0435\u0440\u0448\u0438\u043D\u044B \u043E\u043A\u0430\u0437\u044B\u0432\u0430\u044E\u0442\u0441\u044F \u043C\u0435\u0436\u0434\u0443 \u0434\u0440\u0443\u0433\u0438\u043C\u0438 \u0432\u0435\u0440\u0448\u0438\u043D\u0430\u043C\u0438. \u041D\u0430\u043F\u0440\u0438\u043C\u0435\u0440, \u0432 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"A intermedia\u00E7\u00E3o \u00E9 uma medida de centralidade de um n\u00F3 em uma rede. Ela \u00E9 igual ao n\u00FAmero de menores caminhos de todos os v\u00E9rtices para quaisquer outros v\u00E9rtices que passam por aquele n\u00F3. A intermedia\u00E7\u00E3o \u00E9 uma medida mais \u00FAtil do que apenas a conectividade de um n\u00F3. A primeira \u00E9 mais global para a rede, enquanto a segunda tem apenas um efeito local. O desenvolvimento da intermedia\u00E7\u00E3o \u00E9 geralmente atribu\u00EDdo ao soci\u00F3logo Linton Freeman, que tamb\u00E9m desenvolveu v\u00E1rias outras medidas de centralidade. A mesma ideia tamb\u00E9m foi proposta pelo matem\u00E1tico J. Anthonisse, embora seu trabalho nunca tenha sido publicado. Ao longo dos \u00FAltimos anos, a intermedia\u00E7\u00E3o se tornou uma estrat\u00E9gia popular para lidar com redes complexas. As aplica\u00E7\u00F5es incluem redes sociais e de computadores, (tais como e de poliniza\u00E7\u00E3o), redes de transporte, redes de coopera\u00E7\u00E3o cient\u00EDfica e outras."@pt . . . . . . . . . . . . . . . "In graph theory, betweenness centrality (or \"betweeness centrality\") is a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices such that either the number of edges that the path passes through (for unweighted graphs) or the sum of the weights of the edges (for weighted graphs) is minimized. The betweenness centrality for each vertex is the number of these shortest paths that pass through the vertex."@en . . "En an\u00E1lisis de redes sociales, la centralidad de intermediaci\u00F3n, o simplemente intermediaci\u00F3n (en ingl\u00E9s, betweenness) es una medida de centralidad que cuantifica la frecuencia o el n\u00FAmero de veces que un nodo se encuentra entre las geod\u00E9sicas o caminos m\u00E1s cortos de otros actores. Un actor tendr\u00E1 una alta intermediaci\u00F3n si es un v\u00E9rtice de corte para muchas geod\u00E9sicas entre actores.\u200B\u200B"@es . . . . . . . . "En th\u00E9orie des graphes et th\u00E9orie des r\u00E9seaux, la centralit\u00E9 interm\u00E9diaire, centralit\u00E9 d'interm\u00E9diarit\u00E9 ou interm\u00E9diarit\u00E9 est une mesure de centralit\u00E9 d'un sommet d'un graphe. Elle est \u00E9gale au nombre de fois que ce sommet est sur le chemin le plus court entre deux autres n\u0153uds quelconques du graphe. Un n\u0153ud poss\u00E8de une grande interm\u00E9diarit\u00E9 s'il a une grande influence sur les transferts de donn\u00E9es dans le r\u00E9seau, sous l'hypoth\u00E8se que ces transferts se font uniquement par les chemins les plus courts."@fr . . 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"In graph theory, betweenness centrality (or \"betweeness centrality\") is a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices such that either the number of edges that the path passes through (for unweighted graphs) or the sum of the weights of the edges (for weighted graphs) is minimized. The betweenness centrality for each vertex is the number of these shortest paths that pass through the vertex. Betweenness centrality was devised as a general measure of centrality: it applies to a wide range of problems in network theory, including problems related to social networks, biology, transport and scientific cooperation. Although earlier authors have intuitively described centrality as based on betweenness, gave the first formal definition of betweenness centrality. Betweenness centrality finds wide application in network theory; it represents the degree to which nodes stand between each other. For example, in a telecommunications network, a node with higher betweenness centrality would have more control over the network, because more information will pass through that node."@en .