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An integrated approach: using knowledge graph and network analysis for harnessing digital advertisement

  • 1209: Recent Advances on Social Media Analytics and Multimedia Systems: Issues and Challenges
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Abstract

Complex network analysis helps in finding hidden patterns within a graph network. This concept is extended for knowledge graphs to identify hidden concepts using state-of-the-art network analysis techniques. In this paper, a profiling knowledge graph is analyzed to identify hidden concepts which result in the identification of implicit communities within a campus network. The proposed work is verified with the interesting results achieved by applying different metrics using a state-of-the-art network analysis algorithm. The results of the proposed work are mapped in the domain of digital advertisement to answer intelligent semantic queries. Various factors of centrality measures identify the prospective influencers within a campus network. Moreover, bridge analysis determines amplifier nodes in the knowledge graph that will help in the digital advertisement. The proposed work concludes with a discussion on link prediction. It shows the future interactions to design digital advertising campaigns through billboards.

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Notes

  1. Dbpedia: https://wiki.dbpedia.org/.

  2. YAGO: https://yago-knowledge.org/.

References

  1. Abebe R, Adamic L, Kleinberg J (2018) Mitigating overexposure in viral marketing. AAAI, Menlo Park

  2. Ahmed AA. A privacy-preserving mobile location-based advertising system for small businesses. Eng Rep (3)11. https://doi.org/10.1002/eng2.12416

  3. Alamsyah A, Rahardjo B, Kuspriyanto (2021) Social network analysis taxonomy based on graph representation. ArXiv.;abs/2102.08888

  4. Bashbaghi S, Granger E, Sabourin R, Parchami M (2019) Deep learning architectures for face recognition in video surveillance. In: Jiang X, Hadid A, Pang Y, Granger E, Feng X (eds) Deep Learning in Object Detection and Recognition. Springer Singapore, Singapore, pp 133–154

    Chapter  Google Scholar 

  5. Bauer C, Strauss C (2016) Location-based advertising on mobile devices. Manag Rev Q 66(3):159–194

    Article  Google Scholar 

  6. Benítez-Andrades JA, García-Rodríguez I, Benavides C, Alaiz-Moretón H, Labra Gayo JE (2020) An ontology-based multi-domain model in social network analysis: Experimental validation and case study. Inf Sci 540:390–413

    Article  Google Scholar 

  7. Bhatt S, Padhee S, Sheth A, Chen K, Shalin V, Doran D et al. (2019) Knowledge graph enhanced community detection and characterization. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Melbourne, p 51–9

  8. Bollacker K, Díaz-Rodríguez N, Li X (2019) Extending knowledge graphs with subjective influence networks for personalized fashion. In: Portmann E, Tabacchi ME, Seising R, Habenstein A (eds) Designing Cognitive Cities. Springer International Publishing, Cham, pp 203–233

    Chapter  Google Scholar 

  9. Bonacich P (1986) Power and centrality: a family of measures. Am J Sociol 92(5):1170–1182

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Buccafurri F, Foti VD, Lax G, Nocera A, Ursino D (2013) Bridge analysis in a social internetworking scenario. Inf Sci 224:1–18

    Article  MathSciNet  Google Scholar 

  12. Chartier J-F, Mongeau P, Saint-Charles J (2020) Predicting semantic preferences in a socio-semantic system with collaborative filtering: A case study. Int J Inf Manag 51:102020

    Article  Google Scholar 

  13. Demšar U, Špatenková O, Virrantaus K (2008) Identifying critical locations in a spatial network with graph theory. Trans GIS 12(1):61–82

    Article  Google Scholar 

  14. García-Sánchez F, Colomo-Palacios R, Valencia-García R (2020) A social-semantic recommender system for advertisements. Inf Process Manag 57(2):102153

    Article  Google Scholar 

  15. Golbeck J (2013) Chapter 3 - Network structure and measures. In: Golbeck J (ed) Analyzing the Social Web. Morgan Kaufmann, Boston, pp 25–44

    Chapter  Google Scholar 

  16. Güney E (2019) An efficient linear programming based method for the influence maximization problem in social networks. Inf Sci 503:589–605

    Article  MathSciNet  MATH  Google Scholar 

  17. Hussain N, Wang HH, Buckingham CD, Zhang X (2020) Software agent-centric semantic social network for cyber-physical interaction and collaboration. Int J Software Eng Knowl Eng 30(06):859–893

    Article  Google Scholar 

  18. Karczmarczyk A, Jankowski J, Wątróbski J (2018) Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks. PLoS ONE 13(12):e0209372

    Article  Google Scholar 

  19. Liben-Nowell D, Kleinberg J (2003) The link prediction problem for social networks. Proceedings of the twelfth international conference on Information and knowledge management. Association for Computing Machinery, New Orleans, p 556–9

  20. Lienhart R, Maydt J (2002) An extended set of Haar-like features for rapid object detection. Proceedings International Conference on Image Processing, 1:I-I

  21. Lou K, Yang Y, Wang E, Liu Z, Baker T, Bashir AK (2021) Reinforcement learning based advertising strategy using crowdsensing vehicular data. IEEE Trans Intell Transp Syst 22(7):4635–4647

    Article  Google Scholar 

  22. Michelle Girvan MEJN (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821-78262001

  23. Milovanović S, Bogdanović Z, Labus A, Barać D, Despotović-Zrakić M (2019) An approach to identify user preferences based on social network analysis. Futur Gener Comput Syst 93:121–129

    Article  Google Scholar 

  24. More JS, Lingam C (2019) A gradient-based methodology for optimizing time for influence diffusion in social networks. Soc Netw Anal Min 9(1):5

    Article  Google Scholar 

  25. Munir S, Jami S, Wasi S (2019) A temporal knowledge graph dataset for profiling. Int J Comput Netw Inf Secur 19:193–197

    Google Scholar 

  26. Munir S, Jami SI, Wasi S (2021) Towards the modelling of Veillance based citizen profiling using knowledge graphs. Open Comput Sci 11(1):294–304

    Article  Google Scholar 

  27. Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2):167

    Article  MathSciNet  MATH  Google Scholar 

  28. Paulheim H (2017) Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web 8(3,2017):489–508

    Google Scholar 

  29. Pla Karidi D, Stavrakas Y, Vassiliou Y (2018) Tweet and followee personalized recommendations based on knowledge graphs. J Ambient Intell Humaniz Comput 9(6):2035–2049

    Article  Google Scholar 

  30. Vahdati S, Palma G, Nath RJ, Lange C, Auer S, Vidal M-E (eds) (2018) Unveiling Scholarly Communities over Knowledge Graphs. Digital Libraries for Open Knowledge. Springer International Publishing, Cham

  31. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Article  Google Scholar 

  32. Wakil K, Tahir A, HussnainMQu,Waheed A, Nawaz R (2021) Mitigating urban visual pollution through a multistakeholder spatial decision support system to optimize locational potential of billboards. ISPRS Int J Geo Inf 10(2):60

    Article  Google Scholar 

  33. Wang C, Ma X, Chen J, Chen J (2018) Information extraction and knowledge graph construction from geoscience literature. Comput Geosci 112:112–120

    Article  Google Scholar 

  34. Wasserman SaF K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge, p 201

  35. Zhang L, Luo M, Boncella RJ (2020) Product information diffusion in a social network. Electron Commer Res 20(1):3–19

    Article  Google Scholar 

  36. Zhong C, Arisona SM, Huang X, Batty M, Schmitt G (2014) Detecting the dynamics of urban structure through spatial network analysis. Int J Geogr Inf Sci 28(11):2178–2199

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Mohammad Ali Jinnah University, Karachi, Pakistan

    Siraj Munir & Syed Imran Jami

  2. Department of Computer Science, National University of Computing and Emerging Sciences, Karachi, Pakistan

    Rauf Ahmed Shams Malick & Ghufran Ahmed

  3. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Suleman Khan

  4. Federal University of Piauí (UFPI), Teresina, Brazil

    Joel J. P. C. Rodrigues

  5. Instituto de Telecommunicocoes, Aveiro, Portugal

    Joel J. P. C. Rodrigues

Authors
  1. Siraj Munir
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  2. Rauf Ahmed Shams Malick
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  3. Syed Imran Jami
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  4. Ghufran Ahmed
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  5. Suleman Khan
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  6. Joel J. P. C. Rodrigues
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Correspondence to Siraj Munir.

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Munir, S., Malick, R.A.S., Jami, S.I. et al. An integrated approach: using knowledge graph and network analysis for harnessing digital advertisement. Multimed Tools Appl 82, 8883–8898 (2023). https://doi.org/10.1007/s11042-021-11856-2

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  • DOI: https://doi.org/10.1007/s11042-021-11856-2

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