Abstract
Scale-free networks comprising nodes and edges can be found in various real-world networks. Random walks are used for network exploration since they are essential for understanding the structure of scale-free networks. Agents (random walkers) should reduce the mean first passage time to a target node to increase network exploration efficiency. However, conventional network exploration models cannot flexibly respond to differences in network structures. Conventional models explore networks only using the degree of a current node. In this paper, we propose an exploration model based on a self-avoiding network exploration model. The proposed model selects an appropriate destination node using the local network structure in addition to the degree of a current node. Consequently, the proposed model outperforms than conventional models in respect with the first passage time.
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TS and KT conceived and designed the experiments. KT developed algorithms, performed simulation experiments, and analyzed the data. KT and TS wrote the manuscript.
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Takashima, K., Sakiyama, T. A network exploration model based on memory and local information. Soc. Netw. Anal. Min. 12, 146 (2022). https://doi.org/10.1007/s13278-022-00975-9
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DOI: https://doi.org/10.1007/s13278-022-00975-9