Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution

Area of Science:

• Network Science
• Computational Social Science

Background:

• The small-world phenomenon is prevalent in real-world networks.
• Replicating large networks for analysis is difficult with limited data.
• Understanding network structure and dynamics is crucial.

Purpose of the Study:

• To propose a method for constructing Watts-Strogatz networks from network samples.
• To accurately estimate network metrics like clustering coefficient and degree of separation.
• To address challenges in studying large-scale networks with incomplete data.

Main Methods:

• Utilized a sample from a small-world network with symmetric degree distribution.
• Developed a reconstruction method based on the sampled network data.
• Employed the Watts-Strogatz model as the target network structure.

Main Results:

• The proposed method accurately estimates the degree distribution of Watts-Strogatz networks.
• Network metrics such as clustering coefficient and degree of separation were accurately estimated.
• Reconstruction accuracy improved with an increase in the size of the network sample.

Conclusions:

• The method provides a viable approach for reconstructing large-scale networks from limited data.
• Accurate estimation of network properties is achievable, facilitating further network analysis.
• The findings are significant for studying complex systems where full network data is unavailable.