A novel approach of voterank-based knowledge graph for improvement of multi-attributes influence nodes on social networks

• Autorzy: Pham Hai Van , Duong Pham Van , Tran Dinh Tuan , Lee Joo-Ho

Identyfikatory

DOI

10.2478/jaiscr-2023-0013

• Języki publikacji: EN

Abstrakty

EN

Recently, measuring users and community influences on social media networks play significant roles in science and engineering. To address the problems, many researchers have investigated measuring users with these influences by dealing with huge data sets. However, it is hard to enhance the performances of these studies with multiple attributes together with these influences on social networks. This paper has presented a novel model for measuring users with these influences on a social network. In this model, the suggested algorithm combines Knowledge Graph and the learning techniques based on the vote rank mechanism to reflect user interaction activities on the social network. To validate the proposed method, the proposed method has been tested through homogeneous graph with the building knowledge graph based on user interactions together with influences in realtime. Experimental results of the proposed model using six open public data show that the proposed algorithm is an effectiveness in identifying influential nodes.

Słowa kluczowe

EN

video surveillance; deep learning; moving object detection

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2023
• Tom: Vol. 13. No. 3
• Strony: 165--180
• Opis fizyczny: Bibliogr. 49 poz., rys.

Twórcy

autor

Pham Hai Van

• School of Information Communication and Technology, Hanoi University of Science and Technology, Hanoi, Vietnam

• https://orcid.org/0000-0001-8325-1662

autor

Duong Pham Van

• ICT Department, FPT University, Hanoi, Vietnam

• https://orcid.org/0009-0000-8871-3969

autor

Tran Dinh Tuan

• College of Information Science and Engineering, Ritsumeikan University, Japan

• https://orcid.org/0000-0001-7443-9102

autor

Lee Joo-Ho

• College of Information Science and Engineering, Ritsumeikan University, Japan

• https://orcid.org/0000-0003-1015-5615

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).