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.
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References
Albert R, Jeong H, Barabási AL (2000) Error and attack tolerance of complex networks. Nature 406:378–382
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
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
Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25:163–177
Brandes U (2008) On variants of shortest-path betweenness centrality and their generic computation. Social Netw 30(2):136–145
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
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
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
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
Hoang TBN, Mothe J (2018) Predicting information diffusion on twitter-analysis of predictive features. J Computat Sci 28:257–264
Huang H, Shen H, Meng Z, Chang H, He H (2019) Community-based influence maximization for viral marketing. Appl Intell 49(6):2137–2150
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
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
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
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
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
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
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
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
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
Watts DJ (2002) A simple model of global cascades on random networks. Proc National Acad Sci 99(9):5766–5771
Watts DJ, Dodds PS (2007) Influentials, networks, and public opinion formation. J Consumer Res 34(4):441–458
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
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
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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
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DOI: https://doi.org/10.1007/s13278-022-00865-0