Study on Information Diffusion Analysis in Social Networks and Its Applications

Abstract

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximization and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffusion analysis are listed for further study.

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Acknowledgements

This research was supported by National Natural Science Foundation of China (Nos. 61703386, U1605251 and 91546103), the Anhui Provincial Natural Science Foundation (No. 1708085QF140), the Fundamental Research Funds for the Central Universities (No. WK2150110006), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2014299).

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Correspondence to En-Hong Chen.

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Recommended by Associate Editor Qing-Long Han

Biao Chang received the B. Sc. degree in computer science from University of Science and Technology of China (USTC), China in 2012. He is now a Ph. D. degree candidate at School of Computer Science and Technology of USTC, China under the supervision of professor En-Hong Chen. He also visited Singapore Management Univercity as a research assistant under the supervision of professor Fei-Da Zhu from March 2015 to March 2016. His work has been published in conference proceedings including IJCAI, ICDM, CIKM.

His research interests include social network analysis and recommender systems.

Tong Xu received the Ph. D. degree in University of Science and Technology of China (USTC), China in 2016. He is currently working as a postdoctoral researcher of the Anhui Province Key Laboratory of Big Data Analysis and Application, USTC. He has authored nearly 20 journal and conference papers in the fields of social network and social media analysis, including KDD, AAAI, ICDM, SDM, etc. He was a recipient of the ACM (Hefei) Doctoral Dissertation Award, 2016.

His research interests include social network analysis and data mining.

Qi Liu received the Ph. D. degree in computer science from University of Science and Technology of China, China. He has published prolifically in refereed journals and conference proceedings, e.g., IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, ACM Transactions on Knowledge Discovery from Data, ACM Transactions on Intelligent Systems and Technology, KDD, IJCAI, AAAI, ICDM, SDM, and CIKM. He has served regularly on the program committees of a number of conferences, and is a reviewer for the leading academic journals in his fields. He received the ICDM 2011 Best Research Paper Award and the Best of SDM 2015 Award. He is a member of ACM and the IEEE.

His research interests include data mining and knowledge discovery.

En-Hong Chen received the Ph. D. degree from University of Science and Technology of China. He is a professor and vice dean of School of Computer Science, USTC. He has published more than 150 papers in refereed conferences and journals, including IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Industrial Electronics, KDD, ICDM, NIPS, and CIKM. He was on program committees of numerous conferences including KDD, ICDM, and SDM. He received the Best Application Paper Award on KDD 2008, the Best Research Paper Award on ICDM 2011, and the Best of SDM 2015. His research is supported by the US National Science Foundation for Distinguished Young Scholars of China. He is a senior member of the IEEE.

His research interests includes data mining and machine learning, social network analysis, and recommender systems.

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Chang, B., Xu, T., Liu, Q. et al. Study on Information Diffusion Analysis in Social Networks and Its Applications. Int. J. Autom. Comput. 15, 377–401 (2018). https://doi.org/10.1007/s11633-018-1124-0

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Keywords

  • Information diffusion
  • influence evaluation
  • influence maximization
  • information source detection
  • social network