Copy For Citation
Canlı Usta Ö., Bollt E. M.
ENTROPY, vol.26, no.12, pp.1-16, 2024 (SCI-Expanded)
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Publication Type:
Article / Article
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Volume:
26
Issue:
12
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Publication Date:
2024
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Doi Number:
10.3390/e26121030
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Journal Name:
ENTROPY
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Journal Indexes:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, INSPEC, Metadex, zbMATH, Directory of Open Access Journals, Civil Engineering Abstracts
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Page Numbers:
pp.1-16
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Dokuz Eylül University Affiliated:
Yes
Abstract
Determining causal inference has become popular in physical and engineering applications. While the problem has immense challenges, it provides a way to model the complex networks by observing the time series. In this paper, we present the optimal conditional correlation dimensional geometric information flow principle (oGeoC) that can reveal direct and indirect causal relations in a network through geometric interpretations. We introduce two algorithms that utilize the oGeoC principle to discover the direct links and then remove indirect links. The algorithms are evaluated using coupled logistic networks. The results indicate that when the number of observations is sufficient, the proposed algorithms are highly accurate in identifying direct causal links and have a low false positive rate.