Aba‐Sah Dadzie

29 papers receiving 349 citations

Peers

Aba‐Sah Dadzie
Comparison fields: 5 of 82
  • Artificial Intelligence 252
  • Information Systems 92
  • Computer Vision and Pattern Recognition 76
  • Management Science and Operations Research 63
  • Computer Networks and Communications 55
Replace Mohammed Korayem with:
Mohammed Korayem United States
Mayank Kejriwal United States
Mohammad Aliannejadi Netherlands
Gregory Todd Williams United States
Matthew Honnibal Australia
James Schaffer United States
Christopher Riederer United States
Sheng-yi Kong Taiwan
Robin R. Sewell United States
Denis Kotkov Finland
Aba‐Sah Dadzie relative to Mohammed Korayem United States Mohammed Korayem's profile →
Citations per field
00.5×1.6×
Mohammed Korayem · 1×
Citations per year

Countries citing papers authored by Aba‐Sah Dadzie

Since Specialization
Citations

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

Fields of papers citing papers by Aba‐Sah Dadzie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aba‐Sah Dadzie. 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 Aba‐Sah Dadzie. The network helps show where Aba‐Sah Dadzie may publish in the future.

Co-authorship network of co-authors of Aba‐Sah Dadzie

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 7
3 3
4 5
5
Making Sense of Microposts (#Microposts2016) Social Sciences Track.
2
6 35
7 4
8 10
9
Visual Exploration of Formal Requirements for Data Science Demand Analysis
1
10
Proceedings of the the 4th Workshop on Making Sense of Microposts co-located with the 23rd International World Wide Web Conference (WWW 2014)
2
11
Making sense of microposts (#Microposts2014) named entity extraction & linking challenge
37
12 21
13 3
14
Ageing Factor: a Potential Altmetric for Observing Events and Attention Spans in Social Media.
1
15
Proceedings of the 2nd Workshop on Making Sense of Microposts (#MSM2012):Big things come in small packages
1
16 6
17 0
18 4
19
Doris: managing document-based knowledge in large organisations via semantic web technologies
3
20 6

About Aba‐Sah Dadzie

Aba‐Sah Dadzie is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 30 papers that have together received 389 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (12 papers), Data Visualization and Analytics (11 papers) and Complex Network Analysis Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (252 citations), Management Science and Operations Research (63 citations) and Information Systems (92 citations). Aba‐Sah Dadzie has collaborated with scholars based in United Kingdom, Germany and Slovenia. Frequent co-authors include Matthew Rowe, Milan Stanković, Emmanuel Pietriga, Victoria Uren, Andrea Varga, Vitaveska Lanfranchi, Giuseppe Rizzo, Daniela Petrelli, Inna Novalija and Simon Scerri. Their work appears in journals such as Journal of Cleaner Production, BMC Bioinformatics and BMC Public Health.

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026