Abstract
Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM) is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs) of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA) to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
Highlights
As is well known, many successful artificial intelligence technologies are inspired by certain natural systems or phenomena [1,2,3]
We again have NN = 100 nodes in each test of the simulation, the number of established edges NL in the ripple-spreading network model (RSNM) may vary depending on how fast ripple energy decays, and the comparative BA model is required to generate a network with the same edge number
As a newly reported complex network model, the RSNM may well describe the development of many real-world network systems where the ripple-spreading effect of some local events’ influence plays an important role
Summary
Many successful artificial intelligence technologies are inspired by certain natural systems or phenomena [1,2,3]. New ripples are able to activate other nodes to generate more ripples and to establish new connections between nodes, as long as their point energy is above the relevant thresholds. As this ripple-spreading process goes on for a while, a network topology will appear. The output topology will be largely determined by the values of RSRPs, such as the locations of the epicenters for initial ripples, the thresholds to tell whether a node will be activated or connected, the energy amplifying factor, and the coefficients to define the point energy decaying rate. Once the values for these RSRPs are given and fixed, the output topology will be uniquely determined
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