Abstract：
:Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Shimada Y,Hirata Y,Ikeguchi T,Aihara Kdoi
10.1038/srep34944subject
Has Abstractpub_date
2016-10-11 00:00:00pages
34944issn
2045-2322pii
srep34944journal_volume
6pub_type
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