This paper presents a first-of-its-kind systematic analysis of various backdoor attacks on Graph Convolution Neural Networks (GCNNs). By implementing a wide range of backdoor attack strategies, including trigger node injection, edge modification, feature poisoning, subgraph manipulation, etc., we evaluate the degradation in classification accuracy for target classes and assess the collateral impact on non-target class predictions. Using the widely established Cora and Amazon Co-purchase Network datasets, we provide important case studies and reference points for both attackers and security defenders, sharing essential insights into the severity of each attack method. Our findings highlight the vulnerability of GCNNs to different types of backdoor attacks, underscoring the need for robust defense mechanisms. This work aims to serve as a first-of-its-kind reference for future research in developing and evaluating security measures for GCNNs and GNNs in general.