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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Dbpedia: https://wiki.dbpedia.org/.
YAGO: https://yago-knowledge.org/.
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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