Random Small-world Networks Research Articles

Evolutionary investor sharing game on networks

Investors often co-invest in the same project together. As the payoff of the project realizes, how to share the total payoff is in consideration. With this inspiration, an investor sharing game is proposed in this paper, where investors’ payoffs relate to their investing amount and the degree of monopoly in the industry market. Economically, the degree of monopoly in the market is introduced in the game by a parameter α that affects the evolutionary process. The distribution of investing amounts is assumed to satisfy the Pareto distribution in terms of the empirical wealth distribution. The simulation on WS small-world networks shows that cooperative behaviors will prosper by the union of investors who invest less amounts in a less monopolized market. The higher power of the Pareto distribution of investing amounts also results in more cooperators in equilibriums. Furthermore, as the degree of monopoly swings, the density of cooperators is higher and more stable in a more random small-world network. The findings may be helpful in understanding the effect of network structure on the emergence of cooperation.

Optimal pinning controllability of complex networks: dependence on network structure.

Controlling networked structures has many applications in science and engineering. In this paper, we consider the problem of pinning control (pinning the dynamics into the reference state), and optimally placing the driver nodes, i.e., the nodes to which the control signal is fed. Considering the local controllability concept, a metric based on the eigenvalues of the Laplacian matrix is taken into account as a measure of controllability. We show that the proposed optimal placement strategy considerably outperforms heuristic methods including choosing hub nodes with high degree or betweenness centrality as drivers. We also study properties of optimal drivers in terms of various centrality measures including degree, betweenness, closeness, and clustering coefficient. The profile of these centrality values depends on the network structure. For homogeneous networks such as random small-world networks, the optimal driver nodes have almost the mean centrality value of the population (much lower than the centrality value of hub nodes), whereas the centrality value of optimal drivers in heterogeneous networks such as scale-free ones is much higher than the average and close to that of hub nodes. However, as the degree of heterogeneity decreases in such networks, the profile of centrality approaches the population mean.

Elastic properties of small-world spring networks

We construct small-world spring networks based on a one-dimensional chain and study its static and quasistatic behavior with respect to external forces. Regular bonds and shortcuts are assigned linear springs of constant k and k', respectively. In our models, shortcuts can only stand extensions less than deltac beyond which they are removed from the network. First we consider the simple cases of a hierarchical small-world network and a complete network. In the main part of this paper we study random small-world networks (RSWN) in which each pair of nodes is connected by a shortcut with probability p. We obtain a scaling relation for the effective stiffness of RSWN when k=k'. In this case the extension distribution of shortcuts is scale free with the exponent -2. There is a strong positive correlation between the extension of shortcuts and their betweenness. We find that the chemical end-to-end distance (CEED) could change either abruptly or continuously with respect to the external force. In the former case, the critical force is determined by the average number of shortcuts emanating from a node. In the latter case, the distribution of changes in CEED obeys power laws of the exponent -alpha with alpha < or = 3/2.