Skip to main content
SpringerLink
Log in

Predicting stimulation index of information transmissions by local structural features in social networks

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

In social networking services (SNSs) such as Twitter and Facebook, a variety of information is transmitted among users, and the network grows through the chain of information transmission. The process of information diffusion can be regarded as a dynamic network with users as nodes and information transmission between users as edges. In this study, we propose a stimulation index that serves as an importance index of dynamic edges in information diffusion networks. The stimulation index quantifies the inducement degree of new information transmissions represented as subsequent edges in a dynamic network. Experiments confirm the validity of the index’s stimulation score (STM) as a measure of importance in information diffusion through evaluation experiments using artificial data. In addition, the stimulation index accumulates data and thus enlarges as the network grows. In this work, we focus on the number of candidate nodes that can be directly or indirectly stimulated by an edge, and we propose k-DGC as a feature for edge occurrence. We predict the future stimulation index using k-DGC as input, and the results show consistent performance on a real Twitter dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Albert R, Jeong H, Barabási AL (2000) Error and attack tolerance of complex networks. Nature 406:378–382

    Article  Google Scholar 

  • Arnaboldi V, Conti M, Passarella A, Dunbar RI (2017) Online social networks and information diffusion: the role of ego networks. Online Soc Netw Media 1:44–55

    Article  Google Scholar 

  • Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Political Econ 100(5):992–1026

    Article  Google Scholar 

  • Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25:163–177

    Article  MATH  Google Scholar 

  • Brandes U (2008) On variants of shortest-path betweenness centrality and their generic computation. Social Netw 30(2):136–145

    Article  Google Scholar 

  • Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J (2014) Can cascades be predicted?, In: Proceedings of the 23rd international conference on World wide web, ACM, pp 925–936

  • Chen X, Zhou F, Zhang K, Trajcevski G, Zhong T, Zhang F (2019) Information diffusion prediction via recurrent cascades convolution, In: 2019 IEEE 35th international conference on data engineering (ICDE), IEEE, pp 770–781

  • Clauset A, Moore C, Newman MEJ (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453:98–101

    Article  Google Scholar 

  • Cota W, Ferreira SC, Pastor-Satorras R, Starnini M (2019) Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Sci 8(1):1–13

    Article  Google Scholar 

  • Fushimi T, Satoh T, Saito K, Kazama K (2015) Comparison of Influence Measures on Structural Changes Focused on Node Functions, In: Proceedings of the 17th international conference on information integration and web-based applications & services, iiWAS ’15, ACM, New York, NY, USA, pp. 16:1–16:10. https://doi.org/10.1145/2837185.2837207

  • Gao S, Ma J, Chen Z, Wang G, Xing C (2014) Ranking the spreading ability of nodes in complex networks based on local structure. Phys A: Stat Mech Appl 403:130–147

    Article  MATH  Google Scholar 

  • Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing lett 12(3):211–223

    Article  Google Scholar 

  • Hoang TBN, Mothe J (2018) Predicting information diffusion on twitter-analysis of predictive features. J Computat Sci 28:257–264

    Article  Google Scholar 

  • Huang H, Shen H, Meng Z, Chang H, He H (2019) Community-based influence maximization for viral marketing. Appl Intell 49(6):2137–2150

    Article  Google Scholar 

  • Inafuku K, Fushimi T, Satoh T (2021) Stimulation index of cascading transmission in information diffusion over social networks, In: Complex networks & their applications IX, Springer, Cham, pp 469–481

  • Jain L, Katarya R, Sachdeva S (2019) Role of opinion leader for the diffusion of products using epidemic model in online social network, In: 2019 Twelfth International Conference on Contemporary Computing (IC3), IEEE, pp 1–6

  • Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network, In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 137–146

  • Kim J, Bae J, Hastak M (2018) Emergency information diffusion on online social media during storm Cindy in U.S. Int J Inf Manag 40:153–165

    Article  Google Scholar 

  • Kimura M, Saito K, Nakano R (2007) Extracting influential nodes for information diffusion on a social network, In: AAAI, vol 7, pp 1371–1376

  • Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110

    Article  Google Scholar 

  • Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters, ACM Trans Knowl Discov Data 1(1)

  • Li M, Wang X, Gao K, Zhang S (2017) A survey on information diffusion in online social networks: models and methods. Information 8(4):118

    Article  Google Scholar 

  • Li M, Wang X, Gao K, Zhang S (2017) A survey on information diffusion in online social networks: models and methods, Information (Switzerland) 8

  • Murata T, Koga H (2018) Extended methods for influence maximization in dynamic networks, Comput Soc Netw, 5

  • Osawa S, Murata T (2015) Selecting seed nodes for influence maximization in dynamic networks. Stud Comput Intell 597:91–98

    Article  Google Scholar 

  • Peel L, Clauset A (2014) Detecting change points in the large-scale structure of evolving networks, CoRR arXiv:abs/1403.0989, pp. 2914–2920

  • Rehman AU, Jiang A, Rehman A, Paul A, Sadiq MT et al. (2020) Identification and role of opinion leaders in information diffusion for online discussion network, J Ambient Intell Humanized Comput, 1–13

  • Sheikhahmadi A, Nematbakhsh MA, Zareie A (2017) Identification of influential users by neighbors in online social networks. Phys A Stat Mech Appl 486:517–534

    Article  Google Scholar 

  • Stieglitz S, Dang-Xuan L (2013) Emotions and information diffusion in social media-sentiment of microblogs and sharing behavior. J Manag Inf Syst 29(4):217–248

    Article  Google Scholar 

  • Takashi K, Masashi T, Naoki Y (2016) Detecting information cascades with social influence from microblogs. Inform Process Soc Japan Trans Database 9(2):23–33

    Google Scholar 

  • Ullah F, Lee S (2017) Identification of influential nodes based on temporal-aware modeling of multi-hop neighbor interactions for influence spread maximization. Phys A Stat Mech Appl 486:968–985

    Article  Google Scholar 

  • Varshney D, Kumar S, Gupta V (2017) Predicting information diffusion probabilities in social networks: a Bayesian networks based approach. Knowledge-Based Syst 133:66–76

    Article  Google Scholar 

  • Watts DJ (2002) A simple model of global cascades on random networks. Proc National Acad Sci 99(9):5766–5771

    Article  MathSciNet  MATH  Google Scholar 

  • Watts DJ, Dodds PS (2007) Influentials, networks, and public opinion formation. J Consumer Res 34(4):441–458

    Article  Google Scholar 

  • Yang L, Qiao Y, Liu Z, Ma J, Li X (2018) Identifying opinion leader nodes in online social networks with a new closeness evaluation algorithm. Soft Comput 22(2):453–464

    Article  Google Scholar 

  • Yang J, Counts S (2010) Predicting the speed, scale, and range of information diffusion in twitter, In: Fourth international AAAI conference on weblogs and social media

  • Yuya Y, Kazumi S, Hiroshi M, Kouzou O, Masahiro K (2011) Estimating method of expected influence curve from single diffusion sequence on social networks. IEICE Trans Inf Syst 94(11):1899–1908

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Ibaraki, 305-8550, Japan

    Kazufumi Inafuku & Tetsuji Satoh

  2. School of Computer Science, Tokyo University of Technology, Tokyo, 192-0982, Japan

    Takayasu Fushimi

Authors
  1. Kazufumi Inafuku
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takayasu Fushimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tetsuji Satoh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kazufumi Inafuku.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Inafuku, K., Fushimi, T. & Satoh, T. Predicting stimulation index of information transmissions by local structural features in social networks. Soc. Netw. Anal. Min. 12, 40 (2022). https://doi.org/10.1007/s13278-022-00865-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-022-00865-0

Keywords

Search

Navigation