A Survey on Temporal Graph Representation Learning and Generative Modeling
It addresses the need for advanced techniques in dynamic graph analysis across various domains, but is incremental as it builds on prior work.
The paper surveys neural approaches for temporal graph representation learning and generative modeling, identifying weaknesses in existing methods and proposing a new research direction.
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].