2015
DOI: 10.1038/srep15141
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A new mutually reinforcing network node and link ranking algorithm

Abstract: This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical exper… Show more

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Cited by 15 publications
(20 citation statements)
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References 38 publications
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“…All simulations are carried out using IBM ILOG CPLEX 12.6.2 optimization studio [32]. We consider three well-known network topologies namely, DT10 (10 nodes) [33], NSFNET (14 nodes) [34], and ARPANET (20 nodes) [35]. Each value reported here is calculated as the average of five simulation runs.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…All simulations are carried out using IBM ILOG CPLEX 12.6.2 optimization studio [32]. We consider three well-known network topologies namely, DT10 (10 nodes) [33], NSFNET (14 nodes) [34], and ARPANET (20 nodes) [35]. Each value reported here is calculated as the average of five simulation runs.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Well-designed traffic systems provide passengers with the least time consumption on traveling within destination areas through variable route selections. Reference [12] stated a super edge rank algorithm and its application in identifying opinion leaders of online public opinion supernetwork, and [13] concerned a new mutually reinforcing network node and link ranking algorithm. They applied ranking algorithms mainly on the important nodes and areas to optimize the traffic topology according to the total traffic volume.…”
Section: Relevant Workmentioning
confidence: 99%
“…As for the topology-based importance measures, this paper employs five practical element importance measures that build upon network structure, including connectivity reliability (CR) [28], PageRank [29], Normalized Wide network Ranking algorithm (NWRank) [30], degree centrality [31], and betweenness centrality [11]. The CR-based importance measure monitors the system's CR loss when an element is removed from the network, and quantifies the element importance based on loss levels.…”
Section: Comparison With Other Ranking Measuresmentioning
confidence: 99%
“…HITS) and the weight normalization feature of PageRank. In NWRank, relative weights are assigned to links based on the degree of the adjacent neighbors and their Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank[30]. Simpler metrics, such as the degree centrality, give the highest score of importance to the node with the largest number of neighbors[31,34].…”
mentioning
confidence: 99%