Computer Science ›› 2020, Vol. 47 ›› Issue (5): 7-13.doi: 10.11896/jsjkx.200200071

• Theoretical Computer Science • Previous Articles     Next Articles

Survey on Online Influence Maximization

KONG Fang1, LI Qi-zhi2, LI Shuai3   

  1. 1 School of Software,Shandong University,Jinan 250101,China
    2 School of Computer Science and Technology,Xidian University,Xi'an 710071,China
    3 John Hopcroft Center for Computer Science,Shanghai Jiao Tong University,Shanghai 201100,China
  • Received:2020-02-15 Online:2020-05-15 Published:2020-05-19
  • About author:KONG Fang,born in 1998,undergra-duate.Her main research interests include online influence maximization and so on.
    LI Shuai,born in 1988,Ph.D,assistant professor.Her main research interests include multi-armed bandit,online learning,statistical learning,reinforcement learning,machine learning and bioinformatics.

Abstract: Influence maximization is selecting seed nodes under a given influence propagation model to maximize the information spread.This problem has a wide range of application scenarios,including recommendation systems,viral marketing,information diffusion and link prediction.In practical applications,the node-to-node propagation probabilities in an information propagation model are usually unknown.Besides,online learning algorithms can automatically learn unknown parameters during the interaction process and gradually approach the optimal solution.The paper first discusses the definition of influence maximization problem,introduces commonly used influence propagation models,and summarizes the common offline influence maximization algorithms.Then it introduces the classic online learning framework,the multi-armed bandit setting,analyzes the research status of online influence maximization problem,and compares the performance of common online influence maximization algorithms in real social networks through experiments.Finally,the challenges and research directions of this subject in the future is prospected.

Key words: Influence propagation model, Influence maximization, Social network, Online learning algorithm, Multi-armed bandit

CLC Number: 

  • TP391
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