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Adaptive bridge control strategy for opinion evolution on social networks
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10.1063/1.3602220
/content/aip/journal/chaos/21/2/10.1063/1.3602220
http://aip.metastore.ingenta.com/content/aip/journal/chaos/21/2/10.1063/1.3602220

Figures

Image of FIG. 1.
FIG. 1.

(Color online) A schematic representation of a network with bridge structure. Six star points connect two tight networks with larger intensity, and the two big red nodes denote the nodes with high degree.

Image of FIG. 2.
FIG. 2.

(Color online) Comparison of the controlling effect. The changes of average win percentage of opinion 1, as one increases the probability p that a vacillating node is controlled. The three curves are, respectively, for ER random networks, BA scale free networks, and WS small world networks. The network size is 1000, the average degree of all the networks is 6, and all the data are the average of 1000 independent experiments.

Image of FIG. 3.
FIG. 3.

(Color online) Comparison of the number of controlled nodes. In x-axis, p is the control probability, y-axis denotes the controlled proportion of all nodes. The network size is 1000, the average degree of all the networks is 6, and all the data are the average of 1000 independent experiments.

Image of FIG. 4.
FIG. 4.

(Color online) Visualization-based simulation results. The network has already been occupied by opinion 1, and the green nodes are the nodes which have been controlled during the evolution. We have emphasized them by blue circles.

Image of FIG. 5.
FIG. 5.

(Color online) Comparison of cost and effect. The figure reflects the performance price ratio of our ABC strategy in different kinds of networks. The network size is 1000, the average degree of all networks is 6, and all the data are the average of 1000 independent experiments.

Image of FIG. 6.
FIG. 6.

(Color online) Edge exchanging method: for any network, randomly pick a pair of edge (AB and CD in graph (a), for example) then rewire to have different end nodes (AC and BD as in (b) and AD and BC is also ok). This edge exchanging method can keep each nodes unchanged.

Image of FIG. 7.
FIG. 7.

(Color online) The relationship between the clustering coefficient and the controlling efficiency. The clustering coefficient here is adjusted by the edge exchanging method, so that the degree of each node will remain the same. The network size is 200, the average degree of all networks is 6, and all the data are the average of 250 independent numerical simulations.

Tables

Generic image for table
Table I.

Comparison of clustering coefficient.

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/content/aip/journal/chaos/21/2/10.1063/1.3602220
2011-06-28
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Adaptive bridge control strategy for opinion evolution on social networks
http://aip.metastore.ingenta.com/content/aip/journal/chaos/21/2/10.1063/1.3602220
10.1063/1.3602220
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