Sareh Aghaei

11 total papers · 503 total citations
7 papers, 257 citations indexed

About

Sareh Aghaei is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Sareh Aghaei has authored 7 papers receiving a total of 257 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Information Systems and 1 paper in Computer Networks and Communications. Recurrent topics in Sareh Aghaei's work include Topic Modeling (4 papers), Advanced Graph Neural Networks (3 papers) and Natural Language Processing Techniques (3 papers). Sareh Aghaei is often cited by papers focused on Topic Modeling (4 papers), Advanced Graph Neural Networks (3 papers) and Natural Language Processing Techniques (3 papers). Sareh Aghaei collaborates with scholars based in Austria, Netherlands and Iran. Sareh Aghaei's co-authors include Dieter Fensel, Kevin Angele, Mohammad Reza Khayyambashi, Mohammad Ali Nematbakhsh, Elwin Huaman and Elbrich M. Postma and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Information.

In The Last Decade

Sareh Aghaei

6 papers receiving 228 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Sareh Aghaei	83	81	48	28	25	7	257
Patricia Sachs	56 0.7×	70 0.9×	71 1.5×	36 1.3×	14 0.6×	7	303
Vishal Shah	135 1.6×	59 0.7×	55 1.1×	14 0.5×	13 0.5×	16	301
Bettina Friedl	39 0.5×	54 0.7×	47 1.0×	23 0.8×	8 0.3×	7	324
Alistair Sutcliffe	64 0.8×	65 0.8×	65 1.4×	14 0.5×	9 0.4×	10	268
Jeremy Goecks	130 1.6×	79 1.0×	71 1.5×	33 1.2×	6 0.2×	7	288
Debbie Stone	84 1.0×	43 0.5×	32 0.7×	13 0.5×	30 1.2×	4	297
Ting Lie	58 0.7×	77 1.0×	56 1.2×	26 0.9×	5 0.2×	12	310
Irina Ceaparu	74 0.9×	74 0.9×	12 0.3×	27 1.0×	24 1.0×	9	306
Vishnupriya Das	86 1.0×	132 1.6×	27 0.6×	48 1.7×	25 1.0×	6	312
Pär‐Ola Zander	43 0.5×	55 0.7×	35 0.7×	18 0.6×	30 1.2×	21	264

Countries citing papers authored by Sareh Aghaei

Since Specialization

Citations

This map shows the geographic impact of Sareh Aghaei'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 Sareh Aghaei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sareh Aghaei more than expected).

Fields of papers citing papers by Sareh Aghaei

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sareh Aghaei. 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 Sareh Aghaei. The network helps show where Sareh Aghaei may publish in the future.

Co-authorship network of co-authors of Sareh Aghaei

This figure shows the co-authorship network connecting the top 25 collaborators of Sareh Aghaei. A scholar is included among the top collaborators of Sareh Aghaei 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 Sareh Aghaei. Sareh Aghaei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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