Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Bayesian Artificial Intelligence
2003757 citationsKevin B. Korb, Ann E. Nicholsonprofile →
Bayesian Artificial Intelligence
2010536 citationsKevin B. Korb, Ann E. Nicholsonprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Kevin B. Korb'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 Kevin B. Korb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin B. Korb more than expected).
This network shows the impact of papers produced by Kevin B. Korb. 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 Kevin B. Korb. The network helps show where Kevin B. Korb may publish in the future.
Co-authorship network of co-authors of Kevin B. Korb
This figure shows the co-authorship network connecting the top 25 collaborators of Kevin B. Korb.
A scholar is included among the top collaborators of Kevin B. Korb 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 Kevin B. Korb. Kevin B. Korb is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dorin, Alan & Kevin B. Korb. (2010). Network Measures of Ecosystem Complexity. Artificial Life. 323–328.5 indexed citations
7.
Dorin, Alan, Kevin B. Korb, & Volker Grimm. (2008). Artificial-Life Ecosystems: What are they and what could they become?. Artificial Life. 173–180.14 indexed citations
8.
Woodberry, Owen, Kevin B. Korb, & Ann E. Nicholson. (2007). A simulation study of the evolution of ageing. Evolutionary ecology research. 9(7). 1077–1096.4 indexed citations
9.
Twardy, Charles, Ann E. Nicholson, Kevin B. Korb, & John J. McNeil. (2006). Epidemiological data mining of cardiovascular Bayesian networks. 1(1). 3.19 indexed citations
10.
Woodberry, Owen, Kevin B. Korb, & Ann E. Nicholson. (2005). The Evolution of Aging. 319–333.2 indexed citations
11.
Zukerman, Ingrid, et al.. (1999). Exploratory Interaction with a Bayesian Argumentation System. International Joint Conference on Artificial Intelligence. 1294–1299.10 indexed citations
12.
Korb, Kevin B.. (1999). Calibration and the Evaluation of Predictive Learners. International Joint Conference on Artificial Intelligence. 73–77.3 indexed citations
13.
Korb, Kevin B., et al.. (1999). Bayesian poker. Uncertainty in Artificial Intelligence. 343–350.34 indexed citations
Zukerman, Ingrid, et al.. (1998). Attention During Argument Generation And Presentation..3 indexed citations
16.
Zukerman, Ingrid, et al.. (1998). Bayesian reasoning in an abductive mechanism for argument generation and analysis. National Conference on Artificial Intelligence. 833–838.23 indexed citations
17.
Dai, Honghua, et al.. (1997). A study of causal discovery with weak links and small samples. International Joint Conference on Artificial Intelligence. 1304–1309.21 indexed citations
18.
Korb, Kevin B., et al.. (1997). A cognitive model of argumentation. eScholarship (California Digital Library). 400–405.13 indexed citations
19.
Wallace, Chris S., Kevin B. Korb, & Honghua Dai. (1996). Causal Discovery via MML.. International Conference on Machine Learning. 516–524.44 indexed citations
20.
Dowe, David L., Kevin B. Korb, & Jonathan Oliver. (1996). Information, statistics and induction in science : proceedings of the conference, ISIS '96 : Melbourne, Australia, 20-23 August 1996. WORLD SCIENTIFIC eBooks.
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.