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Complex network data is prevalent in various real-world domains, including physical, technological, and biological systems.
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Richard FitzHugh · 1961
Earlier work this paper cites.
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James L Hindmarsh and RM Rose · 1982
Earlier work this paper cites.
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Benito E Flores · 1986
Earlier work this paper cites.
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David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
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Earlier work this paper cites.
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X-J Wang · 1993
Earlier work this paper cites.
Phase synchronization of chaotic oscillators
Michael G Rosenblum, Arkady S Pikovsky, and Jürgen Kurths · 1996
Earlier work this paper cites.
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Raul González-García, Ramiro Rico-Martìnez, and Ioannis G Kevrekidis · 1998
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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