Kyle Williams

502 citations
20 papers · 280 · h-index 10

Impact in

Papers in

    • Web Data Mining and Analysis 11
    • Information Retrieval and Search Behavior 6
    • Recommender Systems and Techniques 3
    • Expert finding and Q&A systems 2
    • Advanced Text Analysis Techniques 4
    • Topic Modeling 3

Kyle Williams

20 papers receiving 264 citations

Peers

Kyle Williams
Comparison fields: 5 of 50
  • Information Systems and Management 42
  • Information Systems 130
  • Archeology 6
  • Artificial Intelligence 166
  • Computer Science Applications 23
Replace Marc Bron with:
Marc Bron Netherlands
Gheorghe Mureşan United States
Daniel Tunkelang United States
Costantino Thanos Italy
Leo Sauermann Germany
Traugott Koch Sweden
Uri Hanani Israel
Marieke Guy United Kingdom
Stefan Boddie New Zealand
Francesca Carmagnola Italy
Kyle Williams relative to Marc Bron Netherlands Marc Bron's profile →
Citations per field
00.5×2.6×
Marc Bron · 1×
Citations per year

Countries citing papers authored by Kyle Williams

Since Specialization
Citations

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

Fields of papers citing papers by Kyle Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kyle Williams, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kyle Williams Line = papers co-authored together Kyle Williams links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201663
2 201634
3 201430
4 201521
5 201821
6 201319
7 201418
8 201416
9
Cahokia Archaeology: Field Reports
197513
10 201610
11 20158
12 20147
13 20167
14 20144
15 20093
16 20162
17 20141
18 20161
19 20161
20 20161

About Kyle Williams

Kyle Williams is a scholar working on Information Systems, Artificial Intelligence, Management Science and Operations Research, Information Systems and Management and Communication, having authored 20 papers that have together received 280 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (11 papers), Data Quality and Management (6 papers), Information Retrieval and Search Behavior (6 papers), Advanced Text Analysis Techniques (4 papers), Scientific Computing and Data Management (3 papers), Recommender Systems and Techniques (3 papers), Topic Modeling (3 papers) and Expert finding and Q&A systems (2 papers). The work is most often cited by research in Information Systems and Management (42 citations), Information Systems (130 citations), Archeology (6 citations), Artificial Intelligence (166 citations) and Computer Science Applications (23 citations). Kyle Williams has collaborated with scholars based in United States and Netherlands. Frequent co-authors include C. Lee Giles, Imed Zitouni, Aidan C. Crook, Ahmed Hassan Awadallah, Madian Khabsa, Julia Kiseleva, Jian Wu, Tasos Anastasakos, Sagnik Ray Choudhury and Suppawong Tuarob. Their work appears in journals such as International Journal of Digital Earth and Proceedings of the AAAI Conference on Artificial Intelligence.

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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