Influence Maximization with Novelty Decay in Social Networks

Shanshan Feng, Xuefeng Chen, Gao Cong, Yifeng Zeng, Yeow Meng Chee, Yanping Xiang

AAAI 2014 pp. 37-43

doi:10.1609/AAAI.V28I1.8729 /aaai/2014/feng2014aaai-influence/

Abstract

Influence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for solving this problem. However, they have not considered the impact of novelty decay on influence propagation, i.e., repeated exposures will have diminishing influence on users. In this paper, we consider the problem of influence maximization with novelty decay (IMND). We investigate the effect of novelty decay on influence propagation on real-life datasets and formulate the IMND problem. We further analyze the problem properties and propose an influence estimation technique. We demonstrate the performance of our algorithms on four social networks.

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Cite

Text

Feng et al. "Influence Maximization with Novelty Decay in Social Networks." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.8729

Markdown

[Feng et al. "Influence Maximization with Novelty Decay in Social Networks." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/feng2014aaai-influence/) doi:10.1609/AAAI.V28I1.8729

BibTeX

@inproceedings{feng2014aaai-influence,
  title     = {{Influence Maximization with Novelty Decay in Social Networks}},
  author    = {Feng, Shanshan and Chen, Xuefeng and Cong, Gao and Zeng, Yifeng and Chee, Yeow Meng and Xiang, Yanping},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {37-43},
  doi       = {10.1609/AAAI.V28I1.8729},
  url       = {https://mlanthology.org/aaai/2014/feng2014aaai-influence/}
}