Amir Akbarnejad

1.4k citations
6 papers · 233 indexed · h-index 3
Topics
Adversarial Robustness in Machine Learning (4 papers)Advanced Graph Neural Networks (3 papers)Complex Network Analysis Techniques (2 papers)
Partner nations
CanadaGermany

In The Last Decade

Amir Akbarnejad

5 papers receiving 230 citations

Peers

Amir Akbarnejad
Comparison fields: 5 of 39
  • Artificial Intelligence 210
  • Statistical and Nonlinear Physics 55
  • Computer Networks and Communications 40
  • Information Systems 21
  • Computer Vision and Pattern Recognition 21
Replace Amirali Darvishzadeh with:
Amirali Darvishzadeh United States
Giannis Nikolentzos France
Polykarpos Meladianos France
Mostofa Patwary United States
Yongduo Sui China
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Ralitsa Angelova Germany
Bofang Li China
Yan-Li Lee China
Amir Akbarnejad relative to Amirali Darvishzadeh United States Amirali Darvishzadeh's profile →
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Citations per year

Countries citing papers authored by Amir Akbarnejad

Since Specialization
Citations

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

Fields of papers citing papers by Amir Akbarnejad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amir Akbarnejad

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

All Works

6 of 6 papers shown
#WorkIndexed citations
1 2
2 0
3 59
4 2
5 165
6
Adversarial Attacks on Classification Models for Graphs
5

About Amir Akbarnejad

Amir Akbarnejad is a scholar working on Artificial Intelligence, Biophysics and Statistical and Nonlinear Physics, having authored 6 papers that have together received 233 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (4 papers), Advanced Graph Neural Networks (3 papers) and Complex Network Analysis Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (210 citations), Statistical and Nonlinear Physics (55 citations) and Computational Mathematics (2 citations). Amir Akbarnejad has collaborated with scholars based in Canada and Germany. Frequent co-authors include Daniel Zügner, Stephan Günnemann, Oliver Borchert, Nilanjan Ray, Penny J. Barnes and Gilbert Bigras. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, Applied immunohistochemistry & molecular morphology and arXiv (Cornell University).

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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