Maximizing multiple influences and fair seed allocation on multilayer social networks.
Yu Chen1,2, Wei Wang1, Jinping Feng3
1School of Mathematics, Renmin University of China, Beijing, China.
Plos One
|March 13, 2020
Summary
Network owners can now fairly allocate seeds to customers on multilayer networks. This study introduces a new model and greedy solutions for the Fair Seed Allocation (FSA) problem, proving their effectiveness.
Area of Science:
- Network Science
- Information Dissemination
- Computational Social Science
Background:
- Influence maximization is crucial for information spread in networks, but existing methods primarily focus on single networks or participant perspectives.
- Multilayer networks present complex dynamics for influence spread, with limited research on network owner perspectives and fair seed allocation.
- Privacy and corporate interests restrict participant access to network information, necessitating a network owner's holistic view.
Purpose of the Study:
- To address the Fair Seed Allocation (FSA) problem from the perspective of network owners on multilayer networks.
- To propose a novel multiple-influence diffusion model (MMIC) for multilayer networks.
- To develop and theoretically validate greedy algorithms for fair seed allocation in multilayer networks.
Main Methods:
- Proposed the Multiple-Influence Diffusion model on Multilayer networks (MMIC).
- Developed two greedy algorithms to solve the Fair Seed Allocation (FSA) problem on multilayer networks.
- Provided theoretical proofs for the greedy nature of the proposed seed allocation schemes.
Main Results:
- Introduced the MMIC model to capture influence spread dynamics in multilayer networks.
- Demonstrated the effectiveness of the proposed greedy seed allocation schemes through theoretical analysis.
- Experimental validation confirmed the practical efficacy of the developed approaches.
Conclusions:
- The study successfully extends the Fair Seed Allocation problem to multilayer networks from a network owner's viewpoint.
- The proposed MMIC model and greedy algorithms offer effective solutions for influence maximization and fair seed distribution.
- This work provides a foundation for future research in network owner-centric influence management on complex network structures.
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