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Influence maximization on signed networks under independent cascade model

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Abstract

Influence maximization problem is to find a subset of nodes that can make the spread of influence maximization in a social network. In this work, we present an efficient influence maximization method in signed networks. Firstly, we address an independent cascade diffusion model in the signed network (named SNIC) for describing two opposite types of influence spreading in a signed network. We define the independent propagation paths to simulate the influence spreading in SNIC model. Particularly, we also present an algorithm for constructing the set of spreading paths and computing their probabilities. Based on the independent propagation paths, we define an influence spreading function for a seed as well as a seed set, and prove that the spreading function is monotone and submodular. A greedy algorithm is presented to maximize the positive influence spreading in the signed network. We verify our algorithm on the real-world large-scale networks. Experiment results show that our method significantly outperforms the state-of-the-art methods, particularly can achieve more positive influence spreading.

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Acknowledgments

This research was supported in part by the National Natural Science Foundation of China under Grant Nos. 61702441, 61602202, Natural Science Foundation of Jiangsu Province under contracts BK20160428.

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Authors and Affiliations

  1. College of Information Engineering of Yangzhou University, Yangzhou, 225127, China

    Wei Liu, Xin Chen & Ling Chen

  2. The Laboratory for the Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaiyin Institute of Technology, Huaiyin, 223002, China

    Wei Liu & Bolun Chen

  3. School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea

    Wei Liu & Byeungwoo Jeon

Authors
  1. Wei Liu
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  2. Xin Chen
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  3. Byeungwoo Jeon
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  4. Ling Chen
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Correspondence to Wei Liu.

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Liu, W., Chen, X., Jeon, B. et al. Influence maximization on signed networks under independent cascade model. Appl Intell 49, 912–928 (2019). https://doi.org/10.1007/s10489-018-1303-2

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