Abstract
Recently, the issue of offloading cellular data while reducing the duplicated cellular transmission has gained more and more attention. Several studies have shown that sharing contents through Device-to-Device (D2D) to offload traffic to local connections nearby can offer better performance for mobile users. Nevertheless, most existing proposals are somewhat confined to small-scale data sets or limited feature dimensions, relied on unconsolidated hypotheses and measurements of data sets. This paper presents a prior work of large-scale measurements on 3.56 TBytes of real-world data sets, which contain D2D content sharing activities from a popular D2D sharing application (APP). We conduct a comprehensive analysis of multi-dimensional features, including time series, structural properties, meeting dynamics, location relationship, and propagation tree. Our analysis reveals that (i) D2D sharing makes the hops between users shorter (in 3 or 4 degrees), (ii) the maximum spreading distance of content dissemination is 27 hops, (iii) we provide a new evidence of log-normal distribution of all user encounters (named meeting dynamics in this paper) based the fit of inter-TX time, Inter-Content Time (ICT) and Contact Time, (iv) online factor (O) and social factor (S) demonstrate the largest positive correlation and indicate that the two factors have high linear correlation. Finally, we analyze the correlations among all the impact factors by Pearson coefficient, principal component analysis, and latent semantic analysis, respectively. Results reveal that online factor (O) and social factor (S) have a high correlation, especially both of them have a great effect on D2D sharing activities.
Similar content being viewed by others
References
Device-to-Device Communications in 3GPP LTE standard, release 12, http://www.3gpp.org/specifications/releases/68-release-12
Graph-tool, version 2.18, https://graph-tool.skewed.de/
Alduaiji, N., Datta, A., Jianxin, L.: Influence propagation model for clique-based community detection in social networks. IEEE TCSS 5(2), 563–575 (2018)
Aytac, K.N., Girici, T., Yuksel, M., Telli, A., Koksal, I.: Device-to-device caching for video content delivery. IEEE BlackSeaCom, pp.1–2 (2017)
Backstrom, L., Dan, H., Lan, X., Kleinberg, J.: Group Formation in Large Social Networks Membership, Growth, and Evolution. KDD’06, pp. 44–54 (2006)
Backstrom, L., Sun, E., Marlow, C.: Find Me if You Can: Improving Geographical Prediction with Social and Spatial Proximity. ACM WWW, pp. 61–70 (2010)
Bourigault, S., Lamprier, S., Gallinari, P.: Representation Learning for Information Diffusion through Social Networks: an Embedded Cascade Model. ACM WSDM, pp. 573–582 (2016)
Cha, M., Kwak, H., Moon, S., et al.: I tube, you tube, everybody tubes: Analyzing the world’s largest user generated content video system. ACM WWW (2007)
Chang, B., Xu, T., Liu, Q., Chen, E.H.: Study on information diffusion analysis in social networks and its applications. IJAC 15(4), 377–401 (2018)
Chaoji, V., Ranu, S., Rastogi, R., Bhatt, R.: Recommendations To boost content spread in social networks. ACM WWW (2012)
Chen, X., Proulx, B., Gong, X., Zhang, J.: Exploiting social ties for cooperative D2D communications: a mobile social networking case. IEEE/ACM Trans. Netw. 23(5), 1471–1484 (2015)
Cho, E., Myers, S., Leskovec, J.: Friendship and Mobility: User Movement in Location-based Social Networks. ACM KDD’11 (2011)
Cisco: visual networking index: Global Mobile Data Traffic Forecast Update. Technical Report, CISCO Tech. Rep (2014)
Deng, T., Ghafour, A., Pingzhi, F., Di, Y.: Cost-optimal caching for D2D networks with user mobility: modeling, analysis, and computational approaches. IEEE Trans. Wireless Commun. 17(5), 3082–3094 (2018)
Feng, L., Zhao, P., Zhou, F., et al: Resource allocation for 5G D2D multicast content sharing in social-aware cellular networks. IEEE Commun. Mag. 56(3), 112–118 (2018)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. ACM WSDM, pp. 241–250 (2010)
Gulati, A., Eirinaki, M.: Influence propagation for social graph-based recommendations. IEEE Big Data, pp. 2180–2189 (2018)
Han, B, Hui, P, Srinivasan, A., et al: Mobile Data Offloading through Opportunistic Communications and Social Participation. IEEE Trans. Mobi. Comput. (2011)
Han, J., Choi, D., Chun, B., Kwon, T., Kim, H., Choi, Y.: Collecting, Organizing, and Sharing Pins in Pinterest:Interest-driven or Social-driven. ACM SIGMETRICS 42(1), 15–27 (2014)
Han, J., Choi, D., Joo, J., Chuah, C.N.: Predicting Popular and Viral Image Cascades in Pinterest. ICWSM, pp. 82-91 (2017)
Hristova, D., Williams, M.J., Scolo, C.M., et al: Measuring Uban Social Diversity Using Interconnected Geo-Social Networks. ACM WWW pp. 21–30 (2016)
Ioannidis, S., Chaintreau, A., Massoulie, L: Optimal and Scalable Distribution of Content Updates over A Mobile Social Network. IEEE INFOCOM, pp. 1422–1430 (2009)
Jaccard, P.: The distribution of the flora in the alpine zone.1. New Phytologist 11(2), 37–50 (2010)
Jianxin, L, Cai, T., Mian, A., Rong-Hua, L., Sellis, T., Jeffrey, X.Y.: Holistic influence maximization for targeted advertisements in spatial social networks. ICDE, pp. 1340–1343 (2018)
Jianxin, L., Sellis, T., Shane Culpepper, J., Zhenying, H., Chengfei, L., Junhu, W: Geo-social influence spanning maximization. IEEE TKDE 29(8), 1653–1666 (2017)
Jianxin, L., Wang, X., Deng, K., Yang, X., Sellis, T., Xu Yu, J.: Most influential community search over large social networks. IEEE ICDE, pp. 871–882 (2017)
Jin, D., Ma, X., Zhang, Y., Abbas, H., Yu, H.: Information diffusion model based on social big data. Mobile Netw. Appl. 23(4), 717–722 (2018)
Kristian, L., Rumi, G.: Information contagion: an empirical study of the spread of news on digg and twitter social networks. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Mdeia, vol. 52, pp 166–176 (2010)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a Social Network or a News Media. ACM WWW, pp. 591–600 (2010)
Li, Y., Jiang, Y., Jin, D., Su, L., Zeng, L., Wu, D.: Energy-efficient Optimal Opportunistic Forwarding for Delay-Tolerant Networks. IEEE Trans. Veh. Technol. 59 (9), 4500–4512 (2010)
Liu, L., Tang, J., Han, J., Yang, S.: Learning influence from heterogeneous social networks. DMKD 25(3), 511–544 (2012)
Liu, W., Zhang, G., Chen, J., Zou, Y., Ding, W.: Ameasurement-based study on application popularity in android and iOS app stores. ACM Mobidata (2015)
Ma, C., Ding, M., Chen, H., et al: Socially aware caching strategy in device-to-device communication networks. IEEE TVT 67(5), 4615–4629 (2018)
Maghsudi, S., Stanczak, S.: Channel selection for network-assisted d2d communication via no-regret bandit learning with calibrated forecasting. IEEE TWC 14(3), 1309–1322 (2014)
Mandalapu, A.C., Gunabalan, S., Sadineni, A., Cai, T., Haldar, N.A.H., Jianxin, L.: Correlate Influential News Article Events to Stock Quote Movement. ADMA, pp. 331–342 (2019)
Ming, Z., Zeng, Q., Zhu, Y., Jianxin, L., Qian, T.: Sample Location Selection for Efficient Distance-Aware Influence Maximization in Geo-Social Networks. Springer ICDSAA, pp 355–371. Springer, Cham (2018)
Newman, M., The structure and function of complex networks. SIAM Rev. 45(2) (2003)
Rodrigues, T., Benvenuto, F., Cha, M., Gummadi, K., Almeida, V.: On Word-of-Mouth Based Discovery of the Web. ACM IMC (2011)
Sinan, A., Dhillon, P.S.: Social influence maximization under empirical influence models. Nature Human Behaviour 2(6), 375–382 (2018)
Steeg, G., Galstyan, A.: Information Transfer in Social Media. ACM WWW, pp. 509–518 (2012)
Su, C., Guan, X., Du, Y., Huang, X., Zhang, M.: Toward capturing heterogeneity for inferring diffusion networks: A mixed diffusion pattern model. KBS 147, 81–93 (2018)
Susan Dumais, T.: Latent Semantic Analysis. Annual Review of Information Science and Technology 38, 188–230 (2005)
Wang, X., chen, M., Han, Z., Wu, D., Kwon, T.: TOSS: Traffic Offloading by Social Network Service-based Opportunistic Sharing in Mobile Social Networks. IEEE Infocom pp. 2346–2354 (2014)
Wiener, H.: Structural determination of paraffin boiling points. Journal of the American Chemical Society 69(1), 17–20 (1947)
Xuan, Q., Shu, X., Ruan, Z., Wang, J., Fu, C., Chen, G.: A Self-Learning Information Diffusion Model for Smart Social Networks. arXiv:1811.04362 (2018)
Xue, L., Richong, Z., Jianxin, L.: User interest propagation and its application in recommender system. IEEE ICTAI, pp. 218–222 (2017)
Yang, L., Yuan, M., Wang, W., Zhang, Q., Zeng, J.: Apps on the move: A fine-grained analysis of usage behavior of mobile apps. IEEE INFOCOM, pp. 1–9 (2016)
Zhang, X., neglia, G., Towsley, D.: Performance Modeling of Epidemic Routing. Comput. Netw. 51, 2867–2891 (2007)
Zhang, Y., Huang, Z., Wang, S., et al: Spark-Based Measurement and Analysis on Offline Mobile Application Market over Device-to-Device Sharing in Mobile Social Networks. IEEE ICPADS IEEE (2017)
Zhao, X., Yuan, P., Tang, S.: Collaborative edge caching in context-aware device-to-device networks. IEEE TVT 67(10), 9583–9596 (2018)
Zhong, N., Michahelles, F.: Google play is not a long tail market: An empirical analysis of app adoption on the google play app market. ACM Symposium on Applied Computing (SAC’13) (2013)
Acknowledgements
This work was supported by the National Key Research and Development Program of China under Grant 2019YFB2101901 and 2018YFC0809803, the National Natural Science Foundation of China under Grant 61702364, 61972432 and 61972275, and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A02085647).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, X., Wang, C., Chen, X. et al. Measurement and analysis on large-scale offline mobile app dissemination over device-to-device sharing in mobile social networks. World Wide Web 23, 2363–2389 (2020). https://doi.org/10.1007/s11280-020-00807-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-020-00807-w