Classification of vertices on social networks by multiple approaches

Hacı İsmail Aslan; Hoon Ko; Chang Choi; Hacı İsmail Aslan; Hoon Ko; Chang Choi

doi:10.3934/mbe.2022565

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 12146-12159. doi: 10.3934/mbe.2022565

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Classification of vertices on social networks by multiple approaches

1.
Department of Computer Engineering, Gachon University, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea
2.
Research Institute of Computer, Information, Communication, Chungbuk National University, 8-7, Chungdae-ro 1, Seowon-Gu, Cheongju-si, 28644, Chungcheongbuk-do, Republic of Korea

Academic Editor: Yang Kuang

Received: 16 May 2022 Revised: 05 August 2022 Accepted: 11 August 2022 Published: 19 August 2022

Due to the advent of the expressions of data other than tabular formats, the topological compositions which make samples interrelated came into prominence. Analogically, those networks can be interpreted as social connections, dataflow maps, citation influence graphs, protein bindings, etc. However, in the case of social networks, it is highly crucial to evaluate the labels of discrete communities. The reason for such a study is the importance of analyzing graph networks to partition the vertices by only using the topological features of network graphs. For each interaction-based entity, a social graph, a mailing dataset, and two citation sets are selected as the testbench repositories. The research mainly focused on evaluating the significance of three artificial intelligence approaches on four different datasets consisting of vertices and edges. Overall, one of these methods so-called "harmonic functions", resulted in the best form to classify those constituents of graph-shaped datasets. This research not only accessed the most valuable method but also determined how graph neural networks work and the need to improve against non-neural network approaches which are faster and computationally cost-effective. Also in this paper, we will show that there is a limit to be accessed by prospective graph neural network variations by using the topological features of trialed networks.
- graph neural networks,
- graph attention networks,
- harmonic functions,
- node classification,
- social network,
- semi-supervised learning
Citation: Hacı İsmail Aslan, Hoon Ko, Chang Choi. Classification of vertices on social networks by multiple approaches[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12146-12159. doi: 10.3934/mbe.2022565

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Abstract

Due to the advent of the expressions of data other than tabular formats, the topological compositions which make samples interrelated came into prominence. Analogically, those networks can be interpreted as social connections, dataflow maps, citation influence graphs, protein bindings, etc. However, in the case of social networks, it is highly crucial to evaluate the labels of discrete communities. The reason for such a study is the importance of analyzing graph networks to partition the vertices by only using the topological features of network graphs. For each interaction-based entity, a social graph, a mailing dataset, and two citation sets are selected as the testbench repositories. The research mainly focused on evaluating the significance of three artificial intelligence approaches on four different datasets consisting of vertices and edges. Overall, one of these methods so-called "harmonic functions", resulted in the best form to classify those constituents of graph-shaped datasets. This research not only accessed the most valuable method but also determined how graph neural networks work and the need to improve against non-neural network approaches which are faster and computationally cost-effective. Also in this paper, we will show that there is a limit to be accessed by prospective graph neural network variations by using the topological features of trialed networks.

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