  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.
