<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 real-time. Experimental results of the proposed model using six open public data show that the proposed algorithm is an effectiveness in identifying influential nodes.</p>
</abstract>

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 real-time. Experimental results of the proposed model using six open public data show that the proposed algorithm is an effectiveness in identifying influential nodes.

A Novel Approach of Voterank-Based Knowledge Graph for Improvement of Multi-Attributes Influence Nodes on Social Networks

Hanoi University of Science and Technology

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Recently, measuring users and community influences on social media networks play significant roles in science and engineering. To address the problems, many...

A Novel Approach of Voterank-Based Knowledge Graph for Improvement of Multi-Attributes Influence Nodes on Social Networks

Online veröffentlicht: 23. Juni 2023

Seitenbereich: 165 - 180

Eingereicht: 25. Dez. 2022

Akzeptiert: 07. Mai 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0013

Schlüsselwörter
Video surveillance, deep learning, Moving object detection

© 2023 Hai Van Pham et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Novel Approach of Voterank-Based Knowledge Graph for Improvement of Multi-Attributes Influence Nodes on Social Networks

Online veröffentlicht: 23. Juni 2023

Seitenbereich: 165 - 180

Eingereicht: 25. Dez. 2022

Akzeptiert: 07. Mai 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0013

SchlüsselwörterVideo surveillance, deep learning, Moving object detection

© 2023 Hai Van Pham et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Video surveillance, deep learning, Moving object detection