Journal
IEEE SYSTEMS JOURNAL
Volume 14, Issue 2, Pages 1874-1885Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2019.2922373
Keywords
Social networking (online); Estimation; Ions; Integrated circuit modeling; Greedy algorithms; Heuristic algorithms; Optimization; Influence overlapping; influence power; likelihood estimation; opinion maximization; social network
Categories
Funding
- Major International (Regional) Joint Research Project of National Natural Science Foundation of China (NSFC) [71620107003]
- NSFC [61872073, 61572123]
- Program for Liaoning Innovative Research Team in University [LT2016007]
- Fundamental Research Funds for the Central Universities [N171706002]
- China Scholarship Council [201806080095]
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Opinion maximization in social networks is an optimization problem, which targets at determining some influential individuals (i.e., seed nodes), propagating the desired opinion to their neighbors, and eventually obtaining maximum opinion spread. Previous studies assume that influence power of one individual is mainly calculated by using some network structure properties and once the opinion of one individual is determined, its opinion usually keeps unchanged. However, in the real scenario, the influence power of one individual may be unknown and should be closely associated with the dynamic opinion formation process. In this paper, we propose a novel Influence Power-based Opinion Framework (IPOF) to solve the opinion maximization problem, which is composed of two phases: 1) influence power estimation, and 2) elimination of influence overlapping (EIO). Specifically, we design the exponential influence power and estimate the unknown parameter of influence power through maximum likelihood estimation due to its simplicity, practicability, and superior convergence in large samples. To generate the opinion series dynamically, the weighted voter model is proposed by leveraging influence power and intimate degree. Moreover, we also prove that the likelihood function is concave by using Hessian matrix. To determine the initial seed nodes and facilitate large opinion propagation, influence power-based EIO algorithm is proposed. Experimental results in six social networks demonstrate that the proposed approach outperforms the state-of-the-art benchmarks.
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