  In the current era of neural networks and big data, higher dimensional data
is processed for automation of different application areas. Graphs represent a
complex data organization in which dependencies between more than one object or
activity occur. Due to the high dimensionality, this data creates challenges
for machine learning algorithms. Graph convolutional networks were introduced
to utilize the convolutional models concepts that shows good results. In this
context, we enhanced two of the existing Graph convolutional network models by
proposing four enhancements. These changes includes: hyper parameters
optimization, convex combination of activation functions, topological
information enrichment through clustering coefficients measure, and structural
redesign of the network through addition of dense layers. We present extensive
results on four state-of-art benchmark datasets. The performance is notable not
only in terms of lesser computational cost compared to competitors, but also
achieved competitive results for three of the datasets and state-of-the-art for
the fourth dataset.
