Robustness of Multi-Project Knowledge Collaboration Network in Open Source Community.

Area of Science:

• Computer Science
• Social Science
• Network Science

Background:

• Multi-project parallelism is crucial for open source communities (OSCs).
• User collaboration generates significant semantic content, forming knowledge collaboration networks (KCNs).
• Understanding the robustness of these complex networks is vital for OSC development.

Purpose of the Study:

• To investigate the robustness of semantic-based multi-project knowledge collaboration networks in OSCs.
• To analyze how different failure modes (node vs. edge, random vs. deliberate) impact network integrity.
• To differentiate the robustness based on node types (single-project vs. multi-project collaborators).

Main Methods:

• Construction of a directed, weighted, semantic-based multi-project KCN.
• Categorization of nodes into knowledge collaboration and dissemination types based on project involvement.
• Dynamic robustness analysis using node and edge failure modes on empirical data from the Local Motors OSC.

Main Results:

• The KCN exhibits high robustness against random failures but low robustness against deliberate failures.
• The network demonstrates high robustness to edge failures and low robustness to node failures.
• Failure of single-project nodes/edges has less impact than the failure of multi-project nodes/edges.

Conclusions:

• OSCs' KCNs are robust to random disruptions but susceptible to targeted attacks on key multi-project collaborators.
• Network structure and node roles significantly influence robustness, informing targeted management strategies.
• Findings provide insights for enhancing the resilience and promoting the efficient development of open source communities.