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The Propagation Background in Social Networks: Simulating and Modeling

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Abstract

Recent years have witnessed the booming of online social network and social media platforms, which leads to a state of information explosion. Though extensive efforts have been made by publishers to struggle for the limited attention of audiences, still, only a few of information items will be received and digested. Therefore, for simulating the information propagation process, competition among propagating items should be considered, which has been largely ignored by prior works on propagation modeling. One possible reason may be that, it is almost impossible to identify the influence of propagation background from real diffusion data. To that end, in this paper, we design a comprehensive framework to simulate the propagation process with the characteristics of user behaviors and network topology. Specifically, we propose a propagation background simulating (PBS) algorithm to simulate the propagation background by using users′ behavior dynamics and out-degree. Along this line, an ICPB (independent cascade with propagation background) model is adapted to relieve the impact of propagation background by using users′ in-degree. Extensive experiments on kinds of synthetic and real networks have demonstrated the effectiveness of our methods.

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Acknowledgement

This work was supported by National Natural Science Foundation of China (Nos. 91546110, 61703386, 61727809 and U1605251).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Kai Li, Tong Xu, Li-Sheng Qiao & En-Hong Chen

  2. IT Center of Chinese National Audit Office, Beijing, 100073, China

    Shuai Feng & Tian-Yang Lv

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Hua-Wei Shen & Xue-Qi Cheng

Authors
  1. Kai Li
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  2. Tong Xu
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  6. Tian-Yang Lv
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Corresponding author

Correspondence to En-Hong Chen.

Additional information

Kai Li received the B. Sc. and M. Sc. degrees in computer science from the Yan-Shan University and Ji-Lin University, China in 2000 and 2003, respectively. He is now a Ph. D. degree candidate at School of Computer Science and Technology, University of Science and Technology of China (USTC), under the supervision of professor En-Hong Chen. He also visited the Institute of Computing Technology, Chinese Academy of Sciences, China, as a research assistant under the supervision of professor Xue-Qi Cheng from August 2017 to December 2019.

His research interests include social network analysis and human dynamics.

Tong Xu received the Ph. D. degree in computer science from University of Science and Technology of China, China in 2016. He is currently working as an associate researcher of the Anhui Province Key Laboratory of Big Data analysis and Application, USTC. He has authored more than 40 journal and conference papers in the fields of social network and social media analysis, including IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Mobile Computing, 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.

Shuai Feng received the M. Sc. degree in computer science from the Northeast University, China in 2014. He is currently an engineer of the IT center of Chinese National Audit Office.

His research interests includes complex network and audit technology.

Li-Sheng Qiao received the M. Sc. degrees in electric power system & automation from Southwest Jiaotong University, China in 2009. He is now a Ph. D. degree candidate at School of Computer Science and Technology, University of Science and Technology of China, under the supervision of professor En-Hong Chen.

His research interests include deep learning and data mining.

Hua-Wei Shen received the B. Sc. degree in electronic information from the Xi’an Institute of Posts and Telecommunications, China in 2003, and the Ph. D. degree in information security from the Institute of Computing Technology, Chinese Academy of Sciences (ICT-CAS), China in 2010. He is currently a professor in ICT-CAS. He has published more than 20 papers in prestigious journals and top international conferences, including Physical Review E, Journal of Statistical Mechanics, Physica A, WWW, CIKM, and IJCAI. He is a member of the Association of Innovation Promotion for Youth of CAS. He received the Top 100 Doctoral Thesis Award of CAS in 2011 and the Grand Scholarship of the President of CAS in 2010.

His research interests include network science, information recommendation, user behaviour analysis, machine learning, and social network.

Tian-Yang Lv received the Ph. D. degree in computer science from Jilin University, China in 2007. He is currently a senior engineer of IT center of Chinese National Audit Office.

His research interests include complex networks and audit technology.

Xue-Qi Cheng received the Ph. D. degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences, China in 2006. He is a professor in ICT-CAS, and the director of the Research Center of Web Data Science & Engineering (WDSE), ICT-CAS. He has published more than 100 publications in prestigious journals and conferences, including the IEEE Transactions on Information Theory, IEEE Transactions on Knowledge and Data Engineering, Journal of Statistics Mechanics: Theory and Experiment, Physical Review E., ACM SIGIR, WWW, ACM CIKM, WSDM, IJCAI, ICDM, He has won the Best Paper Award in CIKM (2011) and the Best Student Paper Award in SIGIR (2012). He is currently serving on the editorial board of Journal of Computer Science and Technology, Journal of Computer, etc. He received the China Youth Science and Technology Award, 2011, the Young Scientist Award of Chinese Academy of Sciences, 2010, CVIC Software Engineering Award, 2008, the second prize for the National Science and Technology Progress, 2004, etc. He is a member of the IEEE.

His research interests include network science, web search and data mining, big data processing and distributed computing architecture.

En-Hong Chen received the Ph. D. degree from University of Science and Technology of China, China in 1996. 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 National Science Foundation for Distinguished Young Scholars of China. He is a senior member of the IEEE.

His research interests include data mining and machine learning, social network analysis, and recommend systems.

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Li, K., Xu, T., Feng, S. et al. The Propagation Background in Social Networks: Simulating and Modeling. Int. J. Autom. Comput. 17, 353–363 (2020). https://doi.org/10.1007/s11633-020-1227-2

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