MSP-N: Multiple selection procedure with 'N' possible growth mechanisms.

Area of Science:

• Network Science
• Computational Social Science
• Complex Systems

Background:

• Network evolution dynamics present significant modeling challenges.
• Existing models often fail to capture the nuanced growth patterns observed in real-world networks, particularly social networks.

Purpose of the Study:

• To introduce and evaluate a novel network growth mechanism, the multiple-selection-procedure with N options (MSP-N).
• To demonstrate the efficacy of a specific instance, MSP-2, in reconstructing key properties of social networks.

Main Methods:

• Proposed the MSP-N framework where new nodes select one of N growth mechanisms for linking.
• Investigated the MSP-2 model, incorporating mechanisms like preferential attachment, random attachment, node aging, and fitness.
• Evaluated MSP-2 by comparing generated network properties against two real-world social networks.

Main Results:

• MSP-2 successfully reconstructs various degree distributions, including power-law, power-law with exponential cut-off, and exponential distributions.
• Generated networks exhibit high similarity in degree distribution to real-world networks.
• Key network characteristics such as high clustering, triangle count, low spectral radius, and community structure closely match real-world data.

Conclusions:

• The MSP-N framework, particularly MSP-2, offers a powerful approach for modeling complex network evolution.
• This model effectively captures essential structural properties of social networks, outperforming simpler models.
• The findings suggest MSP-2's utility in simulating and understanding the formation of real-world network structures.