Identifying the perceived local properties of networks reconstructed from biased random walks.

ABSTRACT: Many real-world systems give rise to a time series of symbols. The elements in a sequence can be generated by agents walking over a networked space so that whenever a node is visited the corresponding symbol is generated. In many situations the underlying network is hidden, and one aims to recover its original structure and/or properties. For example, when analyzing texts, the underlying network structure generating a particular sequence of words is not available. In this paper, we analyze whether one can recover the underlying local properties of networks generating sequences of symbols for different combinations of random walks and network topologies. We found that the reconstruction performance is influenced by the bias of the agent dynamics. When the walker is biased toward high-degree

SUBMITTER: Guerreiro L 

PROVIDER: S-EPMC10798465 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature