An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector

Agryzkov, Taras; Oliver, José Luis Hervás; Tortosa, Leandro; Vicent, José-Francisco

doi:10.1016/j.amc.2012.08.064

Cited by 57 publications

(52 citation statements)

References 19 publications

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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.…”

Section: The Evaluation Results Of Important Nodesmentioning

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.…”

Section: Related Workmentioning

confidence: 99%

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Node importance evaluation method in wireless sensor network based on energy field model

Sun

Qiao²,

Wang³

et al. 2016

J Wireless Com Network

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The stability degree of key nodes is an important indicator of wireless sensor network performance. Appropriate node importance evaluation method is a precondition for the identification of key node and the analysis on network stability. The current methods based on average length and network density are unable to make real-time evaluation on nodes in practical application. Thus, this paper puts forward a node importance evaluation method in wireless sensor network based on energy field model. Based on the data exchange at the network layer in wireless sensor network, this approach analyzes the node properties and correlation among nodes with energy field and graph theory, which shows the influence result of key nodes in wireless sensor network. Therefore, the node influence can be worked out so as to evaluate the importance of node in wireless sensor network. The experimental results show that, compared with the current node importance evaluation methods of wireless sensor network, the approach proposed in this paper not only is able to evaluate the node importance without affecting the operation of network but also shows the dynamic change in node importance.

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Section: The Evaluation Results Of Important Nodesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Node importance evaluation method in wireless sensor network based on energy field model

Sun

Qiao²,

Wang³

et al. 2016

J Wireless Com Network

View full text Add to dashboard Buy / Rent full text

show abstract

“…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.…”

Section: Network Measuresmentioning

confidence: 99%

EU Ets Facets in the Net: How Account Types Influence the Structure of the System

Borghesi

Flori

2016

SSRN Journal

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In this work, we investigate which countries have been more central during Phases I and II of the European Emission Trading Scheme (EU ETS) with respect to the different types of accounts operating in the system. We borrow a set of centrality measures from Network Theory's tools to describe how the structure of the system has evolved over time and to identify which countries have been in the core or in the periphery of the network. In doing this, we investigate by means of extensive partitions on the different types of accounts and transactions characterizing the EU ETS whether the role of intermediaries (approximated by Person Holding Accounts -PHAs) has affected the overall structure of the system. Preliminary findings over the period [2005][2006][2007][2008][2009][2010][2011][2012] suggest that PHAs have played a prominent role in the transaction of permits, heavily influencing the configuration of the system. This motivates further research on the impact of non-regulated entities in the EU ETS design.

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“…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).…”

Section: The Adapted Pagerank Algorithm (Apa) and Its Characteristicsmentioning

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.…”

Section: Introductionmentioning

confidence: 99%

New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks

Agryzkov

Tortosa

Vicent

2016

Applied Mathematics and Computation

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The Adapted PageRank Algorithm (APA) proposed by Agryzkov et al. provides us a method to establish a ranking of nodes in an urban network. We can say that it constitutes a centrality measure in urban networks, with the main characteristic that is able to consider the importance of data obtained from the urban networks in the process of computing the centrality of every node. Starting from the basic idea of this model, we modify the construction of the matrix used for the classification of the nodes in order of importance. In the APA model, the data matrix is constructed from the original idea of PageRank vector, given an equal chance to jump from one node to another, regardless of the topological distance between nodes. In the new model this idea is questioned. A new matrix with the data network is constructed so that now the data from neighbouring nodes are considered more likely than data from the nodes that are farther away. In addition, this new algorithm has the characteristic that depends on a parameter α, which allows us to decide the importance attached, in the computation of the centrality, to the topology of the network and the amount of data associated with the node. Various numerical experiments with a network of very small size are performed to test the influence of the data associated with the nodes, depending always on the choice of the parameter α. Also we check the differences between the values produced by the original APA model and the new one. Finally, these measures are applied to a real urban network, in which we perform a visual comparison of the results produced by the various measures calculated from the algorithms studied.

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An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector

Cited by 57 publications

References 19 publications

Node importance evaluation method in wireless sensor network based on energy field model

Node importance evaluation method in wireless sensor network based on energy field model

EU Ets Facets in the Net: How Account Types Influence the Structure of the System

New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks

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