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Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph

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Abstract

This study introduces a metric to measure the influence of users and communities on Social Media Networks. The proposed method is a combination of Knowledge Graph and Deep Learning approaches. Particularly, an effective Knowledge Graph is built to represent the interaction activities of users. Besides, an unsupervised deep learning model based on Variational Graph Autoencoder is also constructed to further learn and explore the behavior of users. This model is inspired by conventional Graph Convolutional layers. It is not only able to learn the attribute of users themselves but also enhanced to automatically extract and learn from the relationships among users. The model is robust to unseen data and takes no labeling effort. To ensure the state of the art and fashionable for this work, the dataset is collected by a designed crawling system. The experiments show significant performance and promising results which are competitive and outperforms some well-known Graph-convolutional-based. The proposed approach is applied to build a management system for an influencer marketing campaign, called ADVO system. The ADVO system can detect emerging influencers for a determined brand to run its campaign, and help the brand to manage its campaign. The proposed method is already applied in practice.

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Notes

  1. www.facebook.com

  2. https://af.data.showcase.kyanon.digital/.

References

  • Al-Azim N, Gharib T, Hamdy M, Afify Y (2020) Influence propagation in social networks: interest-based community ranking model. J King Saud Univ Comput Inform Sci. https://doi.org/10.1016/j.jksuci.2020.08.004

    Article  Google Scholar 

  • Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166

    Article  Google Scholar 

  • Bi Y, Wu W et al (2014) A nature-inspired influence propagation model for the community expansion problem. J Combine Optim 28(3):513–528

    Article  MathSciNet  Google Scholar 

  • Bourigault S, Lamprier S, Gallinari P (2016) Representation learning for information diffusion through social networks: an embedded cascade model. In: Proceedings of the 9th ACM international conference on web search and data mining, pp 573–582

  • Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637

    Article  Google Scholar 

  • Chen N (2020) On the approximability of influence in social networks. SIAM J Dis Math 23(3):1400–1415

    Article  MathSciNet  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  Google Scholar 

  • Do N, Nguyen H (2012) A knowledge model about relations and application. In: Proceedings of 4th international conference on data mining and intelligent information technology applications, ICMIA 2012, IEEE, pp 701–704

  • Do N, Nguyen H, Selamat A (2018) Knowledge-based model of expert systems using Rela-model. Int J Softw Eng Knowl Eng 28(8):1047–1090

    Article  Google Scholar 

  • Ester M, Kriegel H-P, Sander J, Xu X, et al. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, KDD 1996, Vol 96, pp 226–231

  • Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch Geometric. In: ICLR workshop on representation learning on graphs and manifolds

  • Ghayour-Baghbani F, Asadpour M, Faili H (2021) Mlpr: efficient influence maximization in linear threshold propagation model using linear programming. Soc Netw Anal Min. https://doi.org/10.1007/s13278-020-00704-0

    Article  Google Scholar 

  • Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the 14th international conference on artificial intelligence and statistics, pp 315–323

  • Gong L, Cheng Q (2019) Exploiting edge features for graph neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9211–9219

  • Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM sigkdd international conference on knowledge discovery and data mining, pp 855–864

  • Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024–1034

  • Hayat MK, Daud A, Alshdadi AA, Banjar A, Abbasi RA, Bao Y, Dawood H (2019) Towards deep learning prospects: insights for social media analytics. IEEE Access 7:36958–36979. https://doi.org/10.1109/ACCESS.2019.2905101

    Article  Google Scholar 

  • He K, et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2016, pp 770–778

  • Hu J, Qian S, Fang Q, Wang Y, Zhao Q, Zhang H, Xu C (2021) Efficient graph deep learning in tensorflow with tf geometric. arXiv preprint arXiv:2101.11552

  • Huang G, Liu Z, et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2017, pp 4700–4708

  • Huynh T, et al (2019) Some measures to detect the influencer on social network based on information propagation. In: Proceedings of the 9th international conference on web intelligence, mining and semantics. New York, NY, USA: Association for Computing Machinery

  • Huynh T et al (2020) Detecting the influencer on social networks using passion point and measures of information propagation. Sustainability, vol 12, no 7

  • Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on machine learning, PMLR 2015, Vol 37, pp 448–456

  • Jiang X, Ji P, Li S (2019) Censnet: convolution with edge-node switching in graph neural networks. In: IJCAI, pp 2656–2662

  • Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM sigkdd international conference on knowledge discovery and data mining, pp 137–146

  • Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representations, ICLR

  • Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: Proceedings of the 2nd international conference on learning representations, ICLR

  • Kipf T, Welling M (2016) Variational graph auto-encoders. In: Nips workshop on bayesian deep learning

  • Kipf T, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Conference track proceedings of 5th international conference on learning representations, ICLR

  • Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86

    Article  MathSciNet  Google Scholar 

  • Leung CK, Cuzzocrea A, Mai JJ, Deng D, Jiang F (2019) Personalized deepinf: enhanced social influence prediction with deep learning and transfer learning. In: 2019 IEEE international conference on big data (big data), pp 2871–2880

  • Ma Y, Wang S, Aggarwal CC, Yin D, Tang J (2019) Multidimensional graph convolutional networks. In: Proceedings of the 2019 siam international conference on data mining, pp 657–665

  • Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th international conference on machine learning, Vol 30, p 3

  • Morone F, Makse HA (2015) Influence maximization in complex networks through optimal percolation. Nature 524(7563):65–68

    Article  Google Scholar 

  • Nguyen H et al (2020) Design a management system for the influencer marketing campaign on social network. In: Phan N, Chellappan S, Choo KR (eds) Computational data and social networks. Springer, Cham, pp 139–151

    Chapter  Google Scholar 

  • Nguyen H, et al. (2020b) Measure of the content creation score on social network using sentiment score and passion point. In: Proceedings of 19th international conference on intelligent software methodologies, tools, and techniques, pp 425–434, IOS press

  • Nickel M, Murphy K, Tresp V, Gabrilovich E (2016) A review of relational machine learning for knowledge graphs. Proc IEEE 104(1):11–33. https://doi.org/10.1109/JPROC.2015.2483592

    Article  Google Scholar 

  • Nurek M, Michalski R (2020) Combining machine learning and social network analysis to reveal the organizational structures. Appl Sci 10(5):1699

    Article  Google Scholar 

  • Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: generalizing degree and shortest paths. Social Netw 32(3):245–251

    Article  Google Scholar 

  • Pei S, Morone F, Makse HA (2018) Theories for influencer identification in complex networks. In: Complex spreading phenomena in social systems, Springer, pp 125–148

  • Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM international conference on knowledge discovery and data mining, SIGKDD, pp 701–710

  • Pezzotti N et al (2017) Approximated and user steerable tSNE for progressive visual analytics. IEEE Trans Visualiz Comput Graph 23(7):1739–1752

    Article  Google Scholar 

  • Qiu J, Tang J, Ma H, Dong Y, Wang K, Tang J (2018) Deepinf: Social influence prediction with deep learning. In: Proceedings of the 24th acm sigkdd international conference on knowledge discovery and data mining, pp 2110–2119

  • Ran Y, Zhang Z et al (2017) Approximation algorithm for partial positive influence problem in social network. J Combine Optim 33(2):791–802

    Article  MathSciNet  Google Scholar 

  • Ribeiro MH, Calais PH, Santos YA, Almeida VA, Meira Jr, W (2018) Like sheep among wolves: characterizing hateful users on twitter. In: MIS2 workshop: misinformation and misbehavior mining on the web. held in conjunction with WSDM 2018

  • Rohini A, SudalaiMuthu T (2020) Machine learning based analysis of influence propagation on social network with time series analysis. In: Proceedings of 2020 fourth international conference on inventive systems and control, ICISC 2020, pp 57–61, IEEE

  • Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Magaz 29(3):93–93

    Article  Google Scholar 

  • Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Y. Bengio & Y. LeCun (Eds.), Proceedings of 3rd international conference on learning representations, ICLR

  • Smith MA, Shneiderman B, Milic-Frayling N, Mendes Rodrigues E, Barash V, Dunne C, Gleave E (2009) Analyzing (social media) networks with nodexl. In: Proceedings of the fourth international conference on communities and technologies, pp 255–264

  • Tran Q M, Nguyen LV, et al. (2019) Efficient cnn models for beer bottle cap classification problem. In: Proceedings of the 6th international conference on future data and security engineering, FDSE, pp 713–721

  • Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph Attention Networks. International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=rJXMpikCZ

  • Wasserman S, Faust K, et al. (1994) Social network analysis: Methods Appl

  • Wu M, Pan S, Zhu X (2020) Openwgl: Open-world graph learning. In: 2020 IEEE international conference on data mining (icdm), pp 681–690

  • Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR, pp 1492–1500

  • Yang Y, Li D (2020) Nenn: Incorporate node and edge features in graph neural networks. In: Asian conference on machine learning, pp 593–608

  • Zhang H, et al. (2020) A social commerce information propagation prediction model based on transformer. In Proceedings of 2nd international conference on computer science communication and network security, CSCNS 2020. MATEC Web Conference Retrieved from https://doi.org/10.1051/matecconf/202133605012

  • Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.2981333

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by Gia Lam Urban Development and Investment Company Limited, Vingroup and supported by Vingroup Innovation Foundation (VINIF) under project code DA132-15062019.

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Author notes

  1. Q. M. Tran and H. D. Nguyen have equally contributed to this work.

Authors and Affiliations

  1. Department of Research and Development, Kyanon Digital, Ho Chi Minh City, Vietnam

    Quan M. Tran

  2. University of Information Technology, Ho Chi Minh City, Vietnam

    Quan M. Tran & Hien D. Nguyen

  3. Vietnam National University, Ho Chi Minh City, Vietnam

    Quan M. Tran & Hien D. Nguyen

  4. Kyanon Digital, Ho Chi Minh City, Vietnam

    Tai Huynh

  5. Department of Data Science, Kyanon Digital, Ho Chi Minh City, Vietnam

    Kha V. Nguyen

  6. Olli Technology, Ho Chi Minh City, Vietnam

    Suong N. Hoang

  7. Sai Gon University, Ho Chi Minh City, Vietnam

    Vuong T. Pham

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  1. Quan M. Tran
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Correspondence to Hien D. Nguyen.

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This paper is a revised and expanded version of a paper entitled Design a management system for the influencer marketing campaign on social network, Proceedings of the 9th International Conference on Computational Data and Social Networks (CSoNet 2020), Dallas, USA, Dec. 2020.

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Tran, Q.M., Nguyen, H.D., Huynh, T. et al. Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph. J Comb Optim 44, 2919–2945 (2022). https://doi.org/10.1007/s10878-021-00815-0

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