Ian Gallagher

478 total citations
3 papers, 6 citations indexed

About

Ian Gallagher is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Geometry and Topology. According to data from OpenAlex, Ian Gallagher has authored 3 papers receiving a total of 6 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Statistical and Nonlinear Physics, 2 papers in Artificial Intelligence and 1 paper in Geometry and Topology. Recurrent topics in Ian Gallagher's work include Complex Network Analysis Techniques (2 papers), Bayesian Methods and Mixture Models (1 paper) and Bayesian Modeling and Causal Inference (1 paper). Ian Gallagher is often cited by papers focused on Complex Network Analysis Techniques (2 papers), Bayesian Methods and Mixture Models (1 paper) and Bayesian Modeling and Causal Inference (1 paper). Ian Gallagher collaborates with scholars based in United Kingdom and United States. Ian Gallagher's co-authors include Gordon N. Dutton, Richard Bowman, A. Jones, Carey E. Priebe, Patrick Rubin‐Delanchy and Kate M. Mitchell and has published in prestigious journals such as Journal of the American Statistical Association and Journal of Visual Impairment & Blindness.

In The Last Decade

Ian Gallagher

3 papers receiving 6 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ian Gallagher United Kingdom	3	3	1	1	1	1	3	6
Arun Kumar Yadav United States	2	3 1.0×					3	3
H. Y. Zhang China	1	4 1.3×					2	4
A. Castro Brazil	2	2 0.7×					4	4
Z. A. Liu China	2	2 0.7×					2	4
S. Godfrey United States	2	2 0.7×		1 1.0×			2	4
D. Hartwig Germany	2	2 0.7×		1 1.0×			2	4
G. Aydın United States	2						2	2
H. A. Weber United States	2						3	2
A. Falabella Switzerland	1						2	2
Y. Yilmaz United States	1						2	2

Countries citing papers authored by Ian Gallagher

Since Specialization

Citations

This map shows the geographic impact of Ian Gallagher's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ian Gallagher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian Gallagher more than expected).

Fields of papers citing papers by Ian Gallagher

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ian Gallagher. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ian Gallagher. The network helps show where Ian Gallagher may publish in the future.

Co-authorship network of co-authors of Ian Gallagher

This figure shows the co-authorship network connecting the top 25 collaborators of Ian Gallagher. A scholar is included among the top collaborators of Ian Gallagher based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ian Gallagher. Ian Gallagher is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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3 of 3 papers shown

1.

Gallagher, Ian, et al.. (2023). Spectral Embedding of Weighted Graphs. Journal of the American Statistical Association. 119(547). 1923–1932. 2 indexed citations

2.

Mitchell, Kate M., et al.. (2012). My Voice Heard: The Journey of a Young Man with a Cerebral Visual Impairment. Journal of Visual Impairment & Blindness. 106(3). 166–176. 2 indexed citations

3.

Gallagher, Ian. (2010). Bayesian block modelling for weighted networks. 55–61. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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