  Temporal graphs represent the dynamic relationships among entities and occur
in many real life application like social networks, e commerce, communication,
road networks, biological systems, and many more. They necessitate research
beyond the work related to static graphs in terms of their generative modeling
and representation learning. In this survey, we comprehensively review the
neural time dependent graph representation learning and generative modeling
approaches proposed in recent times for handling temporal graphs. Finally, we
identify the weaknesses of existing approaches and discuss the research
proposal of our recently published paper TIGGER[24].
