Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

Area of Science:

• Social Network Analysis
• Open Source Systems
• Knowledge Management

Background:

• Open-source projects (OSPs) generate substantial semantic content through designer collaboration.
• Understanding knowledge collaboration dynamics is crucial for OSP development.

Purpose of the Study:

• To construct and analyze a semantic-based knowledge collaborative network in OSPs.
• To identify key opinion leaders and assess network robustness under different degradation scenarios.

Main Methods:

• Constructed a directed, weighted, semantic-based knowledge collaborative network.
• Utilized social network analysis, entropy weight, and TOPSIS to identify opinion leaders.
• Developed three degradation modes and a susceptible-infected-removed (SIR) model for opinion leader influence.
• Conducted dynamic robustness analysis on empirical data from Local Motors OSP community.

Main Results:

• Network robustness varied significantly across degradation modes.
• Degradation of opinion leaders' collaborative behavior resulted in the lowest network robustness.
• Main knowledge dissemination and contribution behaviors showed intermediate robustness.
• Random behavior degradation exhibited the highest network robustness.

Conclusions:

• The study reveals the differential impact of node type degradation on network robustness in OSPs.
• Findings can inform management strategies for open-source communities to ensure stable development.
• Identifying and supporting opinion leaders is critical for maintaining network integrity.