Identifying influential nodes based on graph signal processing in complex networks

Zhao, Jia; Yu, Li; Li, Jingru; Zhou, Peng

doi:10.1088/1674-1056/24/5/058904

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…Zhao et al (2015) suggested the comprehensive consideration of the influence of node characteristics and nodes that are heterogeneous to network topologies. The mining of important nodes is conducted through synthetic evaluation and merging, main eigenvector calculations with a graphical Fourier transform and heterogeneous node integration through graph signal-processing-based centrality.…”

Section: Graph-theoretic Node Importance Mining On Network Topologiesmentioning

confidence: 99%

Graph-theoretic node importance mining in world city networks: methods and applications

Xue

Zhao

2017

IDD

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Purpose This study aims to review the literature on graph-theoretic mining methods for node importance in both static and dynamic world city networks, which is correspondingly categorised by graph-theoretic node importance mining on network topologies and transmission mechanisms. Design/methodology/approach The authors overview the graph-theoretic indicators of node importance: centrality and power. Then, the methods of graph-theoretic node importance mining on network topologies are assessed with node relevance, centrality- and power-based measurements, heterogeneous fusion and other miscellaneous approaches. The latest progress in transmission mechanisms is also reviewed in this study involving network evolution, node immunisation and robustness in dynamics. Finally, the findings are analysed and future directions in this field are suggested. Findings The method development of node importance mining is driven by complex application-based problems within a transmission mechanism. Fusion measurements, based on centrality and power, are extended by other graph mining techniques in which power has a significant role. In conclusion, the trends of node importance mining focus on power-embedded fusion measurements in the transmission mechanism-based complex applications. Originality/value This is the first systematic literature review of node importance from the view of graph-theoretic mining.

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Section: Graph-theoretic Node Importance Mining On Network Topologiesmentioning

confidence: 99%

Graph-theoretic node importance mining in world city networks: methods and applications

Xue

Zhao

2017

IDD

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“…For example, efficient methods for spreading information positively can be employed in warning information spreading in emergency training or realistic emergency scenes. Considerable research has been performed to improve spreading efficiency, such as the shortest spreading paths [1][2][3][4], spreading strategies [5][6][7][8], influential spreader [9][10][11][12][13][14], spreading process [15][16][17][18][19] and spreading behaviours [20,21].…”

Section: Introductionmentioning

confidence: 99%

Improving short-term information spreading efficiency in scale-free networks by specifying top large-degree vertices as the initial spreaders

Wang

Deng

2018

R. Soc. open sci.

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The positive function of initially influential vertices could be exploited to improve spreading efficiency for short-term spreading in scale-free networks. However, the selection of initial spreaders depends on the specific scenes. The selection of initial spreaders needs to offer low complexity and low power consumption for short-term spreading. In this paper, we propose a selection strategy for efficiently spreading information by specifying a set of top large-degree vertices as the initially informed vertices. The essential idea behind the proposed selection strategy is to exploit the significant diffusion of the top large-degree vertices at the beginning of spreading. To evaluate the positive impact of initially influential vertices, we first build an information spreading model in the Barabási–Albert (BA) scale-free network; next, we design 54 comparative Monte Carlo experiments based on a benchmark strategy and the proposed selection strategy in different BA scale-free network structures. Experimental results indicate that (i) the proposed selection strategy can significantly improve spreading efficiency in the short-term spreading and (ii) both network size and number of hubs have a strong impact on spreading efficiency, while the number of initially informed vertices has a weak impact. The proposed selection strategy can be employed in short-term spreading, such as sending warnings or crisis information spreading or information spreading in emergency training or realistic emergency scenes.

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“…Due to their huge utility, how to identify important nodes in complex networks has been a wide concern. [1][2][3] Methods of ranking nodes in complex networks usually use the topological properties of nodes to judge their importance. Commonly, these ranking methods are divided into a single-index ranking method (SRM) and multiple-index ranking method (MRM).…”

Section: Introductionmentioning

confidence: 99%

Ranking important nodes in complex networks by simulated annealing

et al. 2017

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In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented. First, the concept of an importance sequence (IS) to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.

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Identifying influential nodes based on graph signal processing in complex networks

Cited by 14 publications

References 41 publications

Graph-theoretic node importance mining in world city networks: methods and applications

Graph-theoretic node importance mining in world city networks: methods and applications

Improving short-term information spreading efficiency in scale-free networks by specifying top large-degree vertices as the initial spreaders

Ranking important nodes in complex networks by simulated annealing

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