The GIN operator was designed to enhance the expressive power of GNNs by allowing the network to distinguish between graphs that are non-isomorphic, thereby improving performance on tasks requiring precise graph representation . In this work, we tested both the original GIN operator and its modified version that incorporates edges features into the aggregation procedure, GINE , which is defined as