Exploring a New Small-World Network for Real-World Applications

Abstract

Emergent methods for self-organizing a new type of Small-World (SW) network with less average path-length than that obtained with conventional small-world networks are presented. One method is inspired by an Ant-Colony Optimization (ACO) algorithm, and the other is based on a weighted Monte-Carlo generation method for random graphs. The resultant network architecture common to these methods is a multi-star network, which yields a large clustering coefficient and the shortest average path-length among the conventional complex networks such as a Watts-Strogatz model and a Barabási-Albert model etc., from both a theoretical and an experimental analysis of the properties of those networks. Considering the advantageous properties of the multi-star network in real-world applications, it could be used to analyze human networks in SNS such as Twitter and Blog. Another possible application would be in the field of logistics. For example, the conventional airline network could become more efficient and convenient in the future than the current one because of fewer transits and a shorter cruising distance on average from any starting point to any destination on Earth. abstract environment.