Path-wise continuous-time transmission with applications in source identification from partial observations

Qi Zhang (), Chaoyi Shi (), Rui Li and Tianguang Chu ()
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Qi Zhang: School of Information Technology and Management, University of International Business and Economics, Beijing 100029, P. R. China
Chaoyi Shi: College of Engineering, Peking University, Beijing 100871, P. R. China
Rui Li: School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, P. R. China
Tianguang Chu: College of Engineering, Peking University, Beijing 100871, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 04, 1-28

Abstract: Tracing the origin of information spread in social networks is a crucial yet challenging issue. Different from prior studies largely centered on discrete-time transmission frameworks, this paper fulfills source identification for continuous-time diffusion networks. Specifically, a path-wise continuous-time transmission model is first proposed to unveil the probabilistic dynamic through potential paths, based on which the probability of the incomplete cascade under partial observations can be yielded. Finally, a path-wise continuous-time source identification method (PCSI) is proposed under partial-observation conditions and path-wise modelings. To test it, three synthetic networks and two real networks are selected, with series of experiments conducted to evaluate the influence of observation size and cascade number, as well as its robustness to noises. Experiment results verify that our method is able to achieve obviously better source identification accuracies than the previous studies.

Keywords: Source identification; social networks; continuous-time transmission; partial observation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124502097

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