Classification of vertices on social networks by multiple approaches
Not on the map. Why?
Abstract (arXiv)
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 underneath for such a study is the non-negligible importance of analyzing graph networks to partition the vertices by using the topological features of network graphs, solely. For each of these interaction-based entities, a social graph, a mailing dataset, and two citation sets are selected as the testbench repositories. This paper, it was not only assessed 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, this paper showed a limit to be excesses by prospective graph neural network variations by using the topological features of networks trialed.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 13 Jan 2023.
Builds on
16 of 36 references.
References · 16
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4 more
- 2019Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformercited by 88
- 2021Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?cited by 82
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Citing papers per year: 2026: 1.
1 paper.