Multi-stage opinion maximization in social networks

被引:3
作者
He, Qiang [1 ]
Wang, Xingwei [2 ]
Huang, Min [3 ]
Yi, Bo [2 ]
机构
[1] Northeastern Univ, Coll Med & Biol Informat Engn, Shenyang, Liaoning, Peoples R China
[2] Northeastern Univ, Coll Comp Sci & Engn, Shenyang, Liaoning, Peoples R China
[3] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
Opinion maximization; Social network; Activated voter model; Multi-stage algorithm; OBESITY; SPREAD; PEERS;
D O I
10.1007/s00521-021-05840-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Opinion maximization is a crucial optimization approach, which can be used in preventative health, such as heart disease, stroke or diabetes. The key issue of opinion maximization is to select a subset of initial influential individuals (i.e., seed nodes) with the desired opinion, spread the desired opinion to their neighbors and achieve the greatest opinion propagation. Previous researches on opinion maximization focus on user's activation process or static opinions, but pay little attention to the combination between their activation process and dynamic opinion process. In addition, prevalent greedy and heuristic algorithms have some disadvantages, such as low accuracy or low efficiency. In this paper, we study the multi-stage opinion maximization for preventative health in social networks. First, we formulate the opinion maximization problem and leverage the proportion of desired opinions as the objective function. Based on multi-stage independent cascade and weighted voter model, we design the activated voter model to obtain user's activation status and dynamic opinion process. Moreover, we propose a novel Multi-stage Opinion Maximization Scheme (MOMS), which is composed of three phases: (i) the selection of candidate seed nodes, (ii) the generation of seed nodes and (iii) dynamic change of node opinions by the activated voter model. We use an effective heuristic rule to exclude some less essential nodes and select candidate seed nodes. Then, we determine seed nodes of each stage using the improved heuristic algorithm through combining the advantages of heuristic algorithm and greedy algorithm. Finally, experimental results on six social network datasets demonstrate that the proposed method has more superior proportion of desired opinions than the chosen benchmarks.
引用
收藏
页码:12367 / 12380
页数:14
相关论文
共 49 条
  • [1] Opinion Dynamics with Varying Susceptibility to Persuasion
    Abebe, Rediet
    Kleinberg, Jon
    Parkes, David
    Tsourakakis, Charalampos E.
    [J]. KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2018, : 1089 - 1098
  • [2] A Modified Degree Discount Heuristic for Influence Maximization in Social Networks
    Aldawish, Roaa
    Kurdi, Heba
    [J]. 11TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 3RD INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, 2020, 170 : 311 - 316
  • [3] Peers, schools, and adolescent cigarette smoking
    Alexander, C
    Piazza, M
    Mekos, D
    Valente, T
    [J]. JOURNAL OF ADOLESCENT HEALTH, 2001, 29 (01) : 22 - 30
  • [4] [Anonymous], 2017, TWITTER LISTS NETWOR
  • [5] Influence maximization of informed agents in social networks
    AskariSichani, Omid
    Jalili, Mahdi
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2015, 254 : 229 - 239
  • [6] Influence Maximization in Online Social Networks
    Aslay, Cigdem
    Lakshmanan, Laks V. S.
    Lu, Wei
    Xiao, Xiaokui
    [J]. WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2018, : 775 - 776
  • [7] The dissemination of culture - A model with local convergence and global polarization
    Axelrod, R
    [J]. JOURNAL OF CONFLICT RESOLUTION, 1997, 41 (02) : 203 - 226
  • [8] Bakshy E., 2012, P 21 INT C WORLD WID, P519
  • [9] Bao YY, 2013, 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), P1472
  • [10] Identifying influential nodes in complex networks
    Chen, Duanbing
    Lu, Linyuan
    Shang, Ming-Sheng
    Zhang, Yi-Cheng
    Zhou, Tao
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2012, 391 (04) : 1777 - 1787