Real-life graphs from various application domains like social networks, transportation networks, and citation networks evolve over time. These evolving graphs can be modeled as (i) interactions between two nodes in a graph and (ii) interactions associated with a single node. Deep learning techniques using Graph Neural Networks (GNNs) are used for analyzing spatial and temporal properties of graphsfrom these application domains. Analyzing temporal graphs is challenging in comparison to static gra…