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(52 citation statements)

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“…After working out the influence of nodes related to N i , the influence of N i in the entire network can be obtained through expression (11). Considering the iteration in calculation, the initial influence of N i and that of nodes in distance 1 with N i should be excluded.…”

confidence: 99%

“…After working out the influence of nodes related to N i , the influence of N i in the entire network can be obtained through expression (11). Considering the iteration in calculation, the initial influence of N i and that of nodes in distance 1 with N i should be excluded.…”

confidence: 99%

“…In [10], the authors proposed a method based on finite number of objects and ideal objective and evaluated the importance of each node in network through TOPSIS technique which is utilized to aggregate the multi-attribute to obtain the result. In [11], the authors brought in webpage grade vectors in PageRank algorithm to rank the nodes and thus evaluated the node importance. In [12], the authors proposed an evidence-theory-based measurement which could weigh both the degree and strength of every node in a weighted network.…”

confidence: 99%

“…Therefore, to determine the centrality of node i it exploits not only the amount of its incoming links (as approximated for instance by the strength of node i ), but it also considers how its neighbourhood is connected to i. This feature makes the PageRank an appealing indicator and motivates the exploitation of its variants even in several economic and social fields, such as: financial networks and the assessment of systemic risk (Battiston et al, 2012;Hautsch et al, 2014), social networks (Kwak et al, 2010), multiplex networks (Halu et al, 2013), trade networks (Ermann and Shepelyansky, 2011), urban transportation networks (Agryzkov et al, 2012), the ECommerce (Oestreicher-Singer and Sundararajan, 2012), among others.…”

confidence: 99%

“…In [1], Agryzkov et al propose an adaptation of the PageRank model to establish a ranking of nodes in an urban network, taking into account the influence of external activities or information. In the following, we refer to this algorithm as the Adapted Pagerank Algorithm (APA algorithm).…”

confidence: 99%

“…The graph is used to analyse several characteristics of the network and collect statistics, such as the number of incoming and outgoing links of a node, calculation of a strongly connected component of the graph, number of nodes without links, mean diameter of the network and calculation of PageRank. Examples of the calculation of the PageRank is the Adapted PageRank Algorithm (see [1]), in which the authors adapted the concept of PageRank vector to urban networks. In this scenario, the Adapted PageRank Algorithm establishes a ranking of importance of the different nodes in the graph considering external factors to carry out the classification.…”

confidence: 99%