Abstract
Most of the critical real-world networks are con-tinuously changing and evolving with time. Motivated by the growing importance and widespread impact of this type of networks, the dynamic nature of these networks have gained a lot of attention. Because of their intrinsic and special characteristics, these networks are best represented by dynamic graph models. To cope with their evolving nature, the representation model must keep the historical information of the network along with its temporal time. Storing such amount of data, poses many problems from the perspective of dynamic graph data management. This survey provides an in-depth overview on dynamic graph related problems. Novel categorization and classification of the state of the art dynamic graph models are also presented in a systematic and comprehensive way. Finally, we discuss dynamic graph processing including the output representation of its algorithms.
Highlights
Most of the critical real-world networks are continuously changing and evolving with time
Temporal evolution shows how dynamic graphs evolve with time and the changes that happen to its components
We accomplished our goal by explaining the related terminologies of dynamic graph temporal evolution and query operators
Summary
The rest of this paper is organized as follows: Section 2 provides an overview about how dynamic graphs evolve with time.
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