PhysarumSpreader: A New Bio-Inspired Methodology for Identifying Influential Spreaders in Complex Networks

Area of Science:

• Network Science
• Computational Biology
• Complex Systems Analysis

Background:

• Identifying influential spreaders is crucial for optimizing resource allocation and information dissemination in networks.
• The LeaderRank algorithm is an effective random-walk-based method for identifying influential nodes in social networks.
• LeaderRank's original formulation is limited to binary directed networks, necessitating extensions for weighted and undirected network structures.

Purpose of the Study:

• To propose a generalized algorithm, PhysarumSpreader, for identifying influential spreaders in weighted networks.
• To extend the capabilities of LeaderRank by incorporating a positive feedback mechanism inspired by Physarum Polycephalum.
• To develop a method applicable to both directed and undirected weighted networks.

Main Methods:

• A novel algorithm, PhysarumSpreader, was developed by integrating LeaderRank with a positive feedback mechanism.
• The algorithm accounts for edge weights, making it suitable for weighted network analysis.
• PhysarumSpreader was tested on two real-world networks.

Main Results:

• The effectiveness of PhysarumSpreader was demonstrated through comparative analysis with standard centrality measures.
• The proposed method showed superior performance in identifying influential spreaders in weighted networks.
• The algorithm's applicability to both directed and undirected weighted networks was validated.

Conclusions:

• PhysarumSpreader offers an effective and generalized approach for identifying influential spreaders in various weighted network types.
• The integration of a Physarum-inspired positive feedback mechanism enhances the leader identification capabilities.
• This research contributes to advancing network analysis techniques for practical applications in information spreading and resource management.