## 1 Introduction  
The role of Graph Neural Networks (GNNs) has grown significantly in the last 3-4 years for network mining and analysis [1]. Given the ever-evolving data on the social web and the dynamic evolution of network structures, GNN-based frameworks and explorations have improved significantly in contrast to other machine learning algorithms. In fact, Graph Convolutional Networks (GCNs) play a key role in the analysis and information mining of any kind of network structures (biomedical, electrical circuits, etc.). Tasks, such as semi-supervised classification of nodes [2], link prediction [3], graph classification [4], etc., achieve more promising results and predictions based on GCNs and GAT frameworks. The importance of these methods becomes even more apparent when considering that even with 20% or less training data, model accuracy is much higher (over 75%) [2]. The node classification task is basically to find the mapping of nodes 'N' of a graph 'G' to the set of labels 'L'. Mathematically, it can be expressed as \(G:N\to L\). GCNs have been shown to be the most suitable models to perform the task of semi-supervised node classification in different networks. GCN is a message-passing algorithm on graphs. It means that the information about the graphical network propagates through the neighborhood interactions in the graph. In a semi-supervised classification task, the labels for a few nodes in the network are known. Based on this mechanism of message passing, the labels for the remaining nodes are determined. The machine-learning-based models or algorithms are able to use only the information based on the entity features. In contrast, GCN uses the neighborhood interactions, i.e., the information about the features of the node's neighbours (as well as neighbours' neighbours) along with the node features where neighbours are identified using edges. The idea of neighbourhood information aggregation to characterize the underlying node's behaviour is the key aspect of GCN-based models. Thanks to this information propagation, GCN outperforms conventional machine learning models even for a small training dataset. Graph Convolutional Networks (GCN) are preferable for the applications where data are generated from the non-Euclidean space with complex relationships and interdependencies. Traditional machine learning and deep learning strategies over graphical data tend to fail. These algorithms work well with structured data, with each data unit being independent of each other. Real-world networks, such as social networks, biological networks, road traffic networks, etc., are represented in the form of a graph. The graphical representations have an irregular structure where a node can have any number of neighbors. For example, in a social network, one user (node) can have 'x' number of users as friends in the network, while another user can have 'y' number of friends. Hence, the symmetry is not fixed as in the case of an image, where each pixel is represented as a fixed dimension. In addition, each user is related to its friends in the network, while in image data, each pixel unit is independent. In addition, the data for graphs are invariant to the node order. Hence, deep learning frameworks such as CNN are not suitable for operating over graphical data. GCNs do find their application in various application domains. Recently, the GCN framework has been utilized in Google Maps route optimization tasks. The route identification performed by converting the road network of a city into a graph and treating other road activities as features has experimentally shown traffic delays significantly optimized [5]. Protein Folding, another classical problem has also effectively utilized the predictive power of GCNs to estimate the protein molecule interactions. The proposed method not only improves predictions but also lowers the complexity of the task [6]. Similarly, GCN has extensive utility in various other fields, such as computer vision [7], natural language processing [8], medical imaging [9], VLSI domain [10], etc.