An information source localization algorithm based on cellular automata model

Abstract

In order to research the localization problem of information source in a social network, this paper first constructs a complex network node model (Random-Susceptible-Infected-Recovered, R-SIR) based on cellular automata theory, and then utilizes this model to calculate the number of forwarding paths from a node to other forwarding nodes and the probability of occurrence of such forwarding paths, and presents an information source search method based on node reachability measure to locate the information distribution source in the information forwarding network. Theoretical analysis indicates that the algorithm, on the one hand, can avoid the time complexity problem brought by the maximum likelihood estimation, and on the other hand, overcome the inaccuracy problem of step number estimation in accessibility measure. Experimental results show that the algorithm has higher recognition rate and recognition accuracy for the information source in a multi-node network, and can search the information source, and is of great significance for the privacy protection of personal data.

Research on Data Currency Rule and Quality Evaluation

“…In the era of big data and artificial intelligence, personal big data as a valuable asset is growing exponentially. As our various types of data are decentralized in various platforms and systems, the data quality problems caused by obsolescence and inaccurate currency become more and more serious [13,28]. Personal data is a typical kind of dynamic data.…”