TipTop: (Almost) Exact Solutions for Influence Maximization in Billion-Scale Networks

被引:33
作者
Li, Xiang [1 ]
Smith, J. David [2 ]
Dinh, Thang N. [3 ]
Thai, My T. [2 ,4 ,5 ]
机构
[1] Santa Clara Univ, Dept Comp Engn, Santa Clara, CA 95053 USA
[2] Univ Florida, Comp & Informat Sci & Engn Dept, Gainesville, FL 32601 USA
[3] Virginia Commonwealth Univ, Dept Comp Sci, Med Coll Virginia Campus, Richmond, VA 23284 USA
[4] Ton Duc Thang Univ, Div Algorithms & Technol Networks Anal, Ho Chi Minh City, Vietnam
[5] Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam
关键词
Viral marketing; influence maximization; algorithms; online social networks; optimization; ALGORITHM; SET;
D O I
10.1109/TNET.2019.2898413
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we study the cost-aware target viral marketing (CTVM) problem, a generalization of influence maximization. CTVM asks for the most cost-effective users to influence the most relevant users. In contrast to the vast literature, we attempt to offer exact solutions. As the problem is NP-hard, thus, exact solutions are intractable, we propose TIPTOP, a (1-epsilon)-optimal solution for arbitrary epsilon > 0 that scales to very large networks, such as Twitter. At the heart of TIPTOP lies an innovative technique that reduces the number of samples as much as possible. This allows us to exactly solve CTVM on a much smaller space of generated samples using integer programming. Furthermore, TIPTOP lends a tool for researchers to benchmark their solutions against the optimal one in large-scale networks, which is currently not available.
引用
收藏
页码:649 / 661
页数:13
相关论文
共 32 条
  • [1] [Anonymous], Books about us politics
  • [2] Assessing solution quality in stochastic programs
    Bayraksan, Guezin
    Morton, David P.
    [J]. MATHEMATICAL PROGRAMMING, 2006, 108 (2-3) : 495 - 514
  • [3] Borgs M., 2014, P 25 ANN ACM SIAM S, P946, DOI DOI 10.1137/1.9781611973402.70
  • [4] Online Topic-Aware Influence Maximization
    Chen, Shuo
    Fan, Ju
    Li, Guoliang
    Feng, Jianhua
    Tan, Kian-lee
    Tang, Jinhui
    [J]. PROCEEDINGS OF THE VLDB ENDOWMENT, 2015, 8 (06): : 666 - 677
  • [5] Efficient Influence Maximization in Social Networks
    Chen, Wei
    Wang, Yajun
    Yang, Siyu
    [J]. KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2009, : 199 - 207
  • [6] Chen Wei, 2010, P 16 ACM SIGKDD INT, P1029, DOI DOI 10.1145/1835804.1835934
  • [7] Cohen E, 2014, P 23 ACM INT C C INF, P629, DOI [10.1145/2661829.2662077, DOI 10.1145/2661829.2662077]
  • [8] An optimal algorithm for Monte Carlo estimation
    Dagum, P
    Karp, R
    Luby, M
    Ross, S
    [J]. SIAM JOURNAL ON COMPUTING, 2000, 29 (05) : 1484 - 1496
  • [9] SOLUTION OF A LARGE-SCALE TRAVELING-SALESMAN PROBLEM
    DANTZIG, G
    FULKERSON, R
    JOHNSON, S
    [J]. JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF AMERICA, 1954, 2 (04): : 393 - 410
  • [10] On the approximability of positive influence dominating set in social networks
    Dinh, Thang N.
    Shen, Yilin
    Nguyen, Dung T.
    Thai, My T.
    [J]. JOURNAL OF COMBINATORIAL OPTIMIZATION, 2014, 27 (03) : 487 - 503