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

A Survey on Temporal Graph Representation Learning and Generative Modeling