Time-constrained Adaptive Influence Maximization

@article{Tong2020TimeconstrainedAI,
  title={Time-constrained Adaptive Influence Maximization},
  author={Guangmo Tong and R. Wang and Chen Ling and Zheng Dong and Xiang Li},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.01742}
}
  • Guangmo Tong, R. Wang, +2 authors Xiang Li
  • Published 2020
  • Computer Science
  • ArXiv
  • The well-known influence maximization problem aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process. In its adaptive version, additional seed users can be selected after observing certain diffusion results. On the other hand, social computing tasks are often time-critical, and therefore only the influence resulted in the early period is worthwhile, which can be naturally modeled by enforcing a time… CONTINUE READING
    5 Citations
    Improved Approximation Factor for Adaptive Influence Maximization via Simple Greedy Strategies
    • 1
    • PDF
    A Heuristic Algorithm Focusing on the Rich-Club Phenomenon for the Influence Maximization Problem in Social Networks
    Real-Time Influence Maximization in a RTB Setting
    • PDF
    Selecting Influential Features by a Learnable Content-Aware Linear Threshold Model

    References

    SHOWING 1-10 OF 42 REFERENCES
    Adaptive Influence Maximization under General Feedback Models
    • 9
    • PDF
    Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process
    • 214
    • PDF
    Efficient Algorithms for Adaptive Influence Maximization
    • 24
    • PDF
    Adaptive Influence Maximization in Dynamic Social Networks
    • 120
    • PDF
    Time Constrained Influence Maximization in Social Networks
    • 130
    • PDF
    Influence Spreading Path and Its Application to the Time Constrained Social Influence Maximization Problem and Beyond
    • 63
    • PDF
    Influence maximization: near-optimal time complexity meets practical efficiency
    • 495
    • PDF
    Adaptive Submodular Influence Maximization with Myopic Feedback
    • 15
    • PDF
    Adaptive Influence Maximization with Myopic Feedback
    • 11
    • PDF