Lik Mui

1.7k citations
9 papers · 878 · 1 hit paper · h-index 8

Impact in

Papers in

Lik Mui

9 papers receiving 804 citations

Lik Mui's Hit Papers

A computational model of trust and reputation 2003 · 535 citations
5350+7+15Years since publication100200300400500

Peers

Lik Mui
Comparison fields: 5 of 66
  • Information Systems 336
  • Sociology and Political Science 599
  • Artificial Intelligence 421
  • Computer Networks and Communications 273
  • Management Science and Operations Research 101
Replace Giorgos Zacharia with:
Giorgos Zacharia United States
K. Suzanne Barber United States
Aleksandra Korolova United States
Bastin Tony Roy Savarimuthu New Zealand
Ángel Cuevas Spain
Rishabh Mehrotra United Kingdom
Kevin Driscoll United States
Elena Zheleva United States
Ching‐man Au Yeung United Kingdom
Shilad Sen United States
Lik Mui relative to Giorgos Zacharia United States Giorgos Zacharia's profile →
Citations per field
00.5×1.5×1.8×
Giorgos Zacharia · 1×
Citations per year

Countries citing papers authored by Lik Mui

Since Specialization
Citations

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

Fields of papers citing papers by Lik Mui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 6 scholars most cited alongside Lik Mui, 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 Lik Mui Line = papers co-authored together Lik Mui links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1
A computational model of trust and reputation
Hit paper breakdown →
2003535
2 2002155
3 200378
4
Ratings in Distributed Systems: A Bayesian Approach
200267
5 200112
6 199411
7 20079
8
An information theoretic approach to ontology-based interest matching
20018
9 20023

About Lik Mui

Lik Mui is a scholar working on Sociology and Political Science, Artificial Intelligence, Strategy and Management, Information Systems and Management Science and Operations Research, having authored 9 papers that have together received 878 indexed citations. Recurring topics across this work include Evolutionary Game Theory and Cooperation (3 papers), Game Theory and Applications (2 papers), Recommender Systems and Techniques (2 papers), Experimental Behavioral Economics Studies (1 paper), Brain Tumor Detection and Classification (1 paper), Image and Signal Denoising Methods (1 paper), Law, Economics, and Judicial Systems (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Information Systems (336 citations), Sociology and Political Science (599 citations), Artificial Intelligence (421 citations), Computer Networks and Communications (273 citations) and Management Science and Operations Research (101 citations). Lik Mui has collaborated with scholars based in United States. Frequent co-authors include Mojdeh Mohtashemi, Peter Szolovits, Patrick S. P. Wang, Sameer Verma, Arun Agarwal and Amar Gupta. Their work appears in journals such as Journal of Theoretical Biology, International Journal of Pattern Recognition and Artificial Intelligence and DSpace@MIT (Massachusetts Institute of Technology).

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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