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Role of social environment and social clustering in spread of opinions in coevolving networks
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10.1063/1.4833995
/content/aip/journal/chaos/23/4/10.1063/1.4833995
http://aip.metastore.ingenta.com/content/aip/journal/chaos/23/4/10.1063/1.4833995

Figures

Image of FIG. 1.
FIG. 1.

Different types of , where is the probability of the th node accepting the opinion of the th node: (a) “inflexible,” , so that more links will have lower probabilities of accepting opinions; (b) “flexible,” , so that more links will have higher probabilities of accepting opinions.

Image of FIG. 2.
FIG. 2.

A visual representation of the formation of qualitatively distinct consensus states for two different social environments. Both systems start with an initial Watts-Strogatz network (with  = 1000, , and ). (a) Setting and creates an “inflexible” social environment. We observe disintegration of the network into small connected components with each component in internal consensus, i.e., occurs in the network. (b) Setting and creates a “flexible” social environment. We observe a dominant connected component in the final consensus, with size comparable to the initial network, while a large number of the initial opinions go extinct. We refer this kind of final state as a .

Image of FIG. 3.
FIG. 3.

The effect of different social environments on a network of  = 1000 nodes with  = 100 opinions initially present. The starting network is an Erdős-Rényi random network (i.e., clustering ). (a) The distribution of component sizes , the fraction of nodes in the th (ranked by size) connected component in the final consensus state, is plotted as a function of social environment (with marker sizes proportional ). Colors indicate , the clustering coefficient of the th component. The thick bold line in the middle separates the two types of social environment function: on the right we consider flexible social environments, with a single large connected component with size increasing with increasing α; on the left we consider inflexible social environments, observing a decrease of the size of the largest connected component with increasing α, finally leading to its disintegration into several components of comparable sizes. (b) The sizes of the two largest connected components, and , is plotted versus social environment. Simulations were conducted on 100 realizations of the network and initial opinion distribution, with the plotted component sizes estimated as the means over these realizations. Error bars give the standard deviation of these sizes across realizations.

Image of FIG. 4.
FIG. 4.

Properties of the largest connected component () for the final state reached for a flexible environment, , with from an initially Erdős-Rényi network. (a) Comparisons of average path length, maximum degree, clustering coefficient and size for different network sizes, with different markers representing different network sizes (see the legend). The initial network is , with initial clustering close to zero, while here denotes both the largest connected component in the final state and its size (as a fraction of the nodes in the network). We observe that has a significantly higher clustering coefficient (0.2) whereas it has path length comparable to the initial Erdős-Rényi network , implying that has small world features. Also, typically has higher (maximum degree) while its size remains comparable to . (b) The cumulative degree distribution () of the initial network (dashed lines) is compared with that for (markers), further showing that has nodes with higher degrees. In its tail, the cumulative degree distribution of appears to approximately follow a power law as shown by solid grey line of exponent −8 though the steepness of this line does not preclude other distributions in the tail.

Image of FIG. 5.
FIG. 5.

The evolution of system variables with decreasing number of discordant edges. Each variable is plotted at the last time step when that number of discordant edges, , was present in the system. The black line and panel (b) correspond to simulations starting at the highest possible clustering coefficient whereas the red dotted line and panel (a) correspond to simulations starting at the negligible clustering coefficient obtained with a random network of independent edges. In (a) and (b), each color corresponds to one of the opinions, with width indicating the number of nodes holding that opinion. The wide width of cyan at the end in (a) represents the formation of a (one large connected component of size comparable to the initial network). We do not observe a similar transition in (b) even though the only difference in this simulation is the large initial clustering coefficient. In (c), we plot , the size of the largest connected component. Observe the abrupt drop of the black line in , indicating the disintegration of the network into smaller components (i.e., ). In contrast, we do not observe any such transition for the red dotted line, corresponding to the formation of . In (d), we show , the average number of iterations of the system between last-observed times for each , with a substantial increase for the black curve near the end. In (e), is the corresponding evolution of the clustering coefficient.

Image of FIG. 6.
FIG. 6.

A phase diagram for (size of the largest connected component) varying α and in both the inflexible and the flexible social environments. Colors represent the values of (see the color bar). The left panel belongs to the inflexible social environment regime whereas the right panel belongs to the flexible social environment regime. Observe the disintegration of the largest connected components in the flexible social environment regime (the right panel) for higher values of initial clustering (lower values of in shades of red). For lower values of we do not observe any such disintegration (higher values of in shades of blue). In the inflexible regime (the left panel) we observe that values of α dominate the final outcome of the simulation. A network of  = 1000 nodes and with initial opinions was employed for each α and . For visualization, data were interpolated onto a regular grid by a combination of natural neighbor and spline interpolation.

Image of FIG. 7.
FIG. 7.

The effect of different initial clustering on a network of N = 1000 nodes with O = 100 initial opinions for flexible social environment with . Large initial clustering leads to final states with , contrary to the expected for initially unclustered networks in the same flexible social environment. (a) The distribution of component sizes , the fraction of nodes in the th (ranked by size) connected component in the final consensus state, is plotted as a function of the initial clustering coefficient, (with marker sizes proportional ). Colors indicate , the clustering coefficient of the th component in the final state. (b) The sizes of the two largest connected components, and , are plotted versus . Simulations were conducted on 100 realizations of the network and initial opinion distribution, with the plotted component sizes estimated as the means over these realizations. Error bars give the standard deviation of these sizes across realizations.

Image of FIG. 8.
FIG. 8.

Variation of (size of largest connected component) and (size of second largest connected component) with α for social environments, . Different shapes and colors of the markers represent networks of different sizes [see legend in (g)]. In (a) we observe multiple transitions in collapse onto a similar curve (inset) for rescaling α by . A second transition is observed near [dashed grey vertical lines in (b) and (c)], where a best fit to the data changes from a polynomial to power law, as indicated by the values of , the errors between the fitted function and the data points. This second transition appears to be collocated with a transition in appearing in (d), with an abrupt decreasing of after (dashed grey vertical line). Similar to (b) and (c), the best fit to the data changes from a polynomial to power law [see (e) and (f)]. In (g), we plot the Shanon entropy of the 10 largest connected components versus α, observing that tends to saturate near (dashed grey vertical line) and decreases for higher α.

Image of FIG. 9.
FIG. 9.

Variation in the size of the largest connected component with initial clustering coefficient for flexible social environment, , with . When is multiplied to the data for different system sizes appears to collapse onto a single curve. The inset curve shows the fits to data without scaling, with vertical lines indicating the scales of the transition points.

Image of FIG. 10.
FIG. 10.

The sizes of different connected components in the consensus state for networks of  = 1500 nodes. (a) Sizes of connected components v. their ordered (by decreasing size) indices. As initial clustering of the network (color bar) is increased, there is emergence of smaller components of comparable sizes. (b) The values of the exponents, , of the slopes fitted to the sizes of components in the final consensus state v. indices at each value of [the thick red line in (a) is an example for ]. In (b), observe the decrease in the slope and error bars for higher initial clusterings, indicating the formation of several components of comparable sizes.

Tables

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Algorithm 1

A voter model on a coevolving network with clustering and heterogeneous levels of influence.

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/content/aip/journal/chaos/23/4/10.1063/1.4833995
2013-11-20
2014-04-25
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Role of social environment and social clustering in spread of opinions in coevolving networks
http://aip.metastore.ingenta.com/content/aip/journal/chaos/23/4/10.1063/1.4833995
10.1063/1.4833995
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