Perspective Special Issues

A survey on temporal network dynamics with incomplete data


  • Received: 29 May 2022 Revised: 21 July 2022 Accepted: 01 August 2022 Published: 23 August 2022
  • With the development of complex network theory, many phenomena on complex networks, such as infectious disease transmission, information spreading and transportation management, can be explained by temporal network dynamics, to reveal the evolution of the real world. Due to the failure of equipment for collecting data, human subjectivity, and false decisions made by machines when the high accuracy is required, data from temporal networks is usually incomplete, which makes the samples unrepresentative and the model analysis more challenging. This survey concentrates on the pre-processing strategies of incomplete data and overviews two categories of methods on data imputation and prediction, respectively. According to whether each layer in temporal networks has the coupling process, this survey overviews the dynamic modeling approaches in terms of both a single process and coupling processes on complex temporal networks. Moreover, for complex temporal networks with incomplete data, this survey summarizes various characteristic analysis methods, which concentrate on critical nodes identification, network reconstruction, network recoverity, and criticality. Finally, some future directions are discussed for temporal networks dynamics with incomplete data.

    Citation: Xing Wu, Shuai Mao, Luolin Xiong, Yang Tang. A survey on temporal network dynamics with incomplete data[J]. Electronic Research Archive, 2022, 30(10): 3786-3810. doi: 10.3934/era.2022193

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  • With the development of complex network theory, many phenomena on complex networks, such as infectious disease transmission, information spreading and transportation management, can be explained by temporal network dynamics, to reveal the evolution of the real world. Due to the failure of equipment for collecting data, human subjectivity, and false decisions made by machines when the high accuracy is required, data from temporal networks is usually incomplete, which makes the samples unrepresentative and the model analysis more challenging. This survey concentrates on the pre-processing strategies of incomplete data and overviews two categories of methods on data imputation and prediction, respectively. According to whether each layer in temporal networks has the coupling process, this survey overviews the dynamic modeling approaches in terms of both a single process and coupling processes on complex temporal networks. Moreover, for complex temporal networks with incomplete data, this survey summarizes various characteristic analysis methods, which concentrate on critical nodes identification, network reconstruction, network recoverity, and criticality. Finally, some future directions are discussed for temporal networks dynamics with incomplete data.



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