Michael G. Noll

453 citations
12 papers · 178 indexed · h-index 8
Topics
Expert finding and Q&A systems (4 papers)Web Data Mining and Analysis (4 papers)Peer-to-Peer Network Technologies (3 papers)
Journals
IEEE Intelligent SystemsComputational IntelligenceePrints Soton (University of Southampton)

In The Last Decade

Michael G. Noll

12 papers receiving 162 citations

Peers

Michael G. Noll
Comparison fields: 5 of 30
  • Information Systems 134
  • Artificial Intelligence 95
  • Statistical and Nonlinear Physics 31
  • Computer Networks and Communications 29
  • Computer Science Applications 26
Replace Pritam Gundecha with:
Pritam Gundecha United States
Guangyuan Piao Ireland
Henning Rode Netherlands
Shuangyong Song China
Yohei Seki Japan
Luchen Tan Canada
Doris Schiöberg Germany
Valentin Jijkoun Netherlands
Tereza Iofciu Germany
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Countries citing papers authored by Michael G. Noll

Since Specialization
Citations

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

Fields of papers citing papers by Michael G. Noll

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael G. Noll. 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 Michael G. Noll. The network helps show where Michael G. Noll may publish in the future.

Co-authorship network of co-authors of Michael G. Noll

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

All Works

12 of 12 papers shown
#WorkIndexed citations
1 9
2 16
3
Understanding and leveraging the social web for information retrieval
1
4 41
5
On Measuring Expertise in Collaborative Tagging Systems
10
6 8
7 27
8 27
9 4
10 3
11 30
12 2

About Michael G. Noll

Michael G. Noll is a scholar working on Information Systems, Computer Science Applications and Statistical and Nonlinear Physics, having authored 12 papers that have together received 178 indexed citations. Recurring topics across this work include Expert finding and Q&A systems (4 papers), Web Data Mining and Analysis (4 papers) and Peer-to-Peer Network Technologies (3 papers). The work is most often cited by research in Information Systems (134 citations), Computer Science Applications (26 citations) and Artificial Intelligence (95 citations). Michael G. Noll has collaborated with scholars based in Germany, United Kingdom and Luxembourg. Frequent co-authors include Christoph Meinel, Christoph Meinel, Nigel Shadbolt, Nicholas Gibbins, Ching‐man Au Yeung and Charles W. Rice. Their work appears in journals such as IEEE Intelligent Systems, Computational Intelligence and ePrints Soton (University of Southampton).

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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