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Titlebook: Inclusive Smart Cities andDigital Health; 14th International C Carl K. Chang,Lorenzo Chiari,Hamdi Aloulou Conference proceedings 2016 Sprin

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978-3-319-39600-2Springer International Publishing Switzerland 2016
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Inclusive Smart Cities andDigital Health978-3-319-39601-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
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ed on closeness centrality while considering the target city a spatial network. When placing a new facility in a local area, locating it with high accessibility for neighboring residents can attract customers from existing facilities and expand its own trading area. In addition, when there are multi
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ed on closeness centrality while considering the target city a spatial network. When placing a new facility in a local area, locating it with high accessibility for neighboring residents can attract customers from existing facilities and expand its own trading area. In addition, when there are multi
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Jie Yin,Meng Fang,Ghassem Mokhtari,Qing Zhang. It has been observed that the robustness mainly depends on the network structure, the centrality measure, and the type of error. Previous findings regarding the influence of network size on robustness are, however, inconclusive..Based on twenty-four empirical networks, we investigate the relations
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Christian Mandel,Serge Autexier. It has been observed that the robustness mainly depends on the network structure, the centrality measure, and the type of error. Previous findings regarding the influence of network size on robustness are, however, inconclusive..Based on twenty-four empirical networks, we investigate the relations
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of social network users). To achieve this, approaches often start with a few example community members and employ graph filters that rank nodes based on their structural proximity to the examples. Choosing between well-known filters typically involves experiments on existing graphs, but their effic
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