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.
Finding scientific topics
20044.1k citationsThomas L. Griffiths, Mark Steyversprofile →
The author-topic model for authors and documents
2004877 citationsMark Steyvers, Padhraic Smyth et al.profile →
The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
2005850 citationsMark Steyvers et al.Cognitive Scienceprofile →
Topics in semantic representation.
2007709 citationsThomas L. Griffiths, Mark Steyvers et al.Psychological Reviewprofile →
A model for recognition memory: REM—retrieving effectively from memory
This map shows the geographic impact of Mark Steyvers'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 Mark Steyvers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Steyvers more than expected).
This network shows the impact of papers produced by Mark Steyvers. 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 Mark Steyvers. The network helps show where Mark Steyvers may publish in the future.
Co-authorship network of co-authors of Mark Steyvers
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Steyvers.
A scholar is included among the top collaborators of Mark Steyvers 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 Mark Steyvers. Mark Steyvers is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Steyvers, Mark, et al.. (2020). An Aha! Walks into a Bar: Joke Completion as a Form of Insight Problem Solving.. Cognitive Science.1 indexed citations
11.
Steyvers, Mark, et al.. (2019). It's not the treasure, it's the hunt: Children are more explorative on an explore/exploit task than adults.. Cognitive Science. 2891–2897.5 indexed citations
12.
Benjamin, Aaron S., et al.. (2017). A Bayesian model of knowledge and metacognitive control: Applications to opt-in tasks.. Cognitive Science.2 indexed citations
13.
Beckage, Nicole, Mark Steyvers, & Carter T. Butts. (2012). Route choice in individuals—semantic network navigation. Cognitive Science. 34(34).6 indexed citations
14.
Steyvers, Mark, et al.. (2011). Using Inverse Planning and Theory of Mind for Social Goal Inference.. Cognitive Science. 33(33).11 indexed citations
15.
Lee, Michael, et al.. (2011). A Model-Based Approach to Measuring Expertise in Ranking Tasks.. Cognitive Science. 33(33).8 indexed citations
16.
Steyvers, Mark, et al.. (2010). Wisdom of the Crowds in Minimum Spanning Tree Problems. eScholarship (California Digital Library). 32(32).7 indexed citations
17.
Smyth, Padhraic, et al.. (2010). Learning concept graphs from text with stick-breaking priors. Neural Information Processing Systems. 23. 334–342.6 indexed citations
18.
Steyvers, Mark, Brent Miller, Pernille Hemmer, & Michael Lee. (2009). The Wisdom of Crowds in the Recollection of Order Information. Neural Information Processing Systems. 22. 1785–1793.48 indexed citations
19.
Griffiths, Thomas L., Michael Lee, Danielle Navarro, & Mark Steyvers. (2005). Modeling Individual Differences with Dirichlet Processes. eScholarship (California Digital Library). 27(27).2 indexed citations
20.
Griffiths, Thomas L. & Mark Steyvers. (2002). Prediction and Semantic Association. Neural Information Processing Systems. 15. 11–18.51 indexed citations
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.