  Many real-world phenomena can be modeled as a graph, making them extremely
valuable due to their ubiquitous presence. GNNs excel at capturing those
relationships and patterns within these graphs, enabling effective learning and
prediction tasks. GNNs are constructed using Multi-Layer Perceptrons (MLPs) and
incorporate additional layers for message passing to facilitate the flow of
features among nodes. It is commonly believed that the generalizing power of
GNNs is attributed to the message-passing mechanism between layers, where nodes
exchange information with their neighbors, enabling them to effectively capture
and propagate information across the nodes of a graph. Our technique builds on
these results, modifying the message-passing mechanism further: one by weighing
the messages before accumulating at each node and another by adding Residual
connections. These two mechanisms show significant improvements in learning and
faster convergence
