  A limitation of the Graph Convolutional Networks (GCNs) is that it assumes at
a particular $l^{th}$ layer of the neural network model only the $l^{th}$ order
neighbourhood nodes of a social network are influential. Furthermore, the GCN
has been evaluated on citation and knowledge graphs, but not extensively on
friendship-based social graphs. The drawback associated with the dependencies
between layers and the order of node neighbourhood for the GCN can be more
prevalent for friendship-based graphs. The evaluation of the full potential of
the GCN on friendship-based social network requires openly available datasets
in larger quantities. However, most available social network datasets are not
complete. Also, the majority of the available social network datasets do not
contain both the features and ground truth labels. In this work, firstly, we
provide a guideline on simulating dynamic social networks, with ground truth
labels and features, both coupled with the topology. Secondly, we introduce an
open-source Python-based simulation library. We argue that the topology of the
network is driven by a set of latent variables, termed as the social DNA
(sDNA). We consider the sDNA as labels for the nodes. Finally, by evaluating on
our simulated datasets, we propose four new variants of the GCN, mainly to
overcome the limitation of dependency between the order of node-neighbourhood
and a particular layer of the model. We then evaluate the performance of all
the models and our results show that on 27 out of the 30 simulated datasets our
proposed GCN variants outperform the original model.

Machine Learning;Social and Information Networks;Machine Learning