Information Geometry

Aims & Scope

This journal will publish original work in the emerging interdisciplinary field of information geometry, with both a theoretical and computational emphasis.

Information geometry connects various branches of mathematical science in dealing with uncertainty and information based on unifying geometric concepts.

Furthermore, it demonstrates the great potential of abstract thinking and corresponding formalisms within many application fields.

Theoretical topics of interest will include, but are not limited to, the Fisher–Rao metric, the Amari–Chentsov tensor, alpha geometry, dual connections, exponential and mixture geodesics, divergence functions, information and entropy functions, convex analysis, Hessian geometry, information projections, q-statistics and deformed exponential/logarithm, algebraic statistics, optimal transport geometry, and related topics.

The authors and audience of this journal will be interdisciplinary, coming from the many disciplines that inspire the development of information-geometric methods and benefit from their application, including mathematics, statistics, machine learning, neuroscience, information theory, statistical and quantum physics, control theory and optimization, complex networks and systems, theoretical biology, cognitive science, mathematical finance, and allied disciplines.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.2

Journal Rank

Year	Value
2024	16398

Journal Citation Indicator

Year	Value
2024	74

SJR (SCImago Journal Rank)

Year	Value
2024	0.308

Quartile

Year	Value
2024	Q3

h-index

Year	Value
2024	11

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Wasserstein Riemannian geometry of Gaussian densities

  Citation: 52

  Authors: Luigi, Luigi, Giovanni

• Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem

  Citation: 52

  Authors: Shun-ichi, Ryo, Masafumi

• Natural gradient via optimal transport

  Citation: 41

  Authors: Wuchen, Guido

• Logarithmic divergences from optimal transport and Rényi geometry

  Citation: 37

  Authors: Ting-Kam Leonard

• Geometric thermodynamics for the Fokker–Planck equation: stochastic thermodynamic links between information geometry and optimal transport

  Citation: 27

  Authors: Sosuke

• Rho–tau embedding and gauge freedom in information geometry

  Citation: 20

  Authors: Jan, Jun

• Active learning by query by committee with robust divergences

  Citation: 17

  Authors: Hideitsu, Shinto

• Entropy-regularized 2-Wasserstein distance between Gaussian measures

  Citation: 17

  Authors: Anton, Augusto, Hà Quang

• Transport information geometry: Riemannian calculus on probability simplex

  Citation: 14

  Authors: Wuchen

• Optimal transport natural gradient for statistical manifolds with continuous sample space

  Citation: 13

  Authors: Yifan, Wuchen

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