  Graph neural network (GNN) is a popular tool to learn the lower-dimensional
representation of a graph. It facilitates the applicability of machine learning
tasks on graphs by incorporating domain-specific features. There are various
options for underlying procedures (such as optimization functions, activation
functions, etc.) that can be considered in the implementation of GNN. However,
most of the existing tools are confined to one approach without any analysis.
Thus, this emerging field lacks a robust implementation ignoring the highly
irregular structure of the real-world graphs. In this paper, we attempt to fill
this gap by studying various alternative functions for a respective module
using a diverse set of benchmark datasets. Our empirical results suggest that
the generally used underlying techniques do not always perform well to capture
the overall structure from a set of graphs.
