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.
This map shows the geographic impact of Mark Burstein'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 Burstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Burstein more than expected).
This network shows the impact of papers produced by Mark Burstein. 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 Burstein. The network helps show where Mark Burstein may publish in the future.
Co-authorship network of co-authors of Mark Burstein
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Burstein.
A scholar is included among the top collaborators of Mark Burstein 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 Burstein. Mark Burstein is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Benton, J., et al.. (2016). Active Perception for Cyber Intrusion Detection and Defense.. National Conference on Artificial Intelligence.1 indexed citations
2.
McDonald, David D., et al.. (2016). Extending Biology Models with Deep NLP over Scientific Articles.. National Conference on Artificial Intelligence.3 indexed citations
3.
Musliner, David J., et al.. (2012). FUZZBUSTER: A System for Self-Adaptive Immunity from Cyber Threats. 118–123.6 indexed citations
4.
Grosof, Benjamin N., et al.. (2010). A SILK graphical UI for defeasible reasoning, with a biology causal process example. 113–116.2 indexed citations
5.
Yaman, Fusun, Tim Oates, & Mark Burstein. (2009). A context driven approach for workflow mining. International Joint Conference on Artificial Intelligence. 1798–1803.7 indexed citations
6.
Burstein, Mark, Robert Laddaga, David D. McDonald, et al.. (2008). POIROT: integrated learning of web service procedures. National Conference on Artificial Intelligence. 1274–1279.16 indexed citations
Burstein, Mark, Christoph Bußler, Tim Finin, et al.. (2005). A semantic Web services architecture. IEEE Internet Computing. 9(5). 72–81.133 indexed citations
McDermott, Drew, Mark Burstein, & Douglas R. Smith. (2001). Overcoming ontology mismatches in transactions with self-describing service. International Semantic Web Conference. 285–302.6 indexed citations
Burstein, Mark & Douglas R. Smith. (1996). ITAS: a portable, interactive transportation scheduling tool using a search engine generated from formal specifications. 35–44.8 indexed citations
Burstein, Mark & Allan M. Collins. (1988). Modeling a Theory of Human Plausible Reasoning.. 21–28.12 indexed citations
17.
Burstein, Mark, et al.. (1987). Implementing a model of human plausible reasoning. International Joint Conference on Artificial Intelligence. 185–188.4 indexed citations
18.
Burstein, Mark. (1986). Concept Formation by Incremental Analogical Reasoning and Debugging. Machine Learning. 2. 351–368.62 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.