This map shows the geographic impact of Denver Dash'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 Denver Dash with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Denver Dash more than expected).
This network shows the impact of papers produced by Denver Dash. 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 Denver Dash. The network helps show where Denver Dash may publish in the future.
Co-authorship network of co-authors of Denver Dash
This figure shows the co-authorship network connecting the top 25 collaborators of Denver Dash.
A scholar is included among the top collaborators of Denver Dash 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 Denver Dash. Denver Dash is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dash, Denver, et al.. (2010). Relational learning for collective classification of entities in images. National Conference on Artificial Intelligence. 7–12.6 indexed citations
7.
Dash, Denver, et al.. (2010). Learning Causal Models That Make Correct Manipulation Predictions.. neural information processing systems. 257–266.
8.
Margineantu, Dragos D., Weng‐Keen Wong, & Denver Dash. (2010). A special issue of Machine Learning.1 indexed citations
Dash, Denver, et al.. (2008). Learning causal models that make correct manipulation predictions with time series data. D-Scholarship@Pitt (University of Pittsburgh). 257–266.1 indexed citations
11.
Dash, Denver, et al.. (2007). COD: online temporal clustering for outbreak detection. National Conference on Artificial Intelligence. 633–638.4 indexed citations
12.
Dash, Denver, Branislav Kveton, John Mark Agosta, et al.. (2006). When gossip is good: distributed probabilistic inference for detection of slow network intrusions. National Conference on Artificial Intelligence. 1115–1122.42 indexed citations
Dash, Denver. (2005). Restructuring Dynamic Causal Systems in Equilibrium.. International Conference on Artificial Intelligence and Statistics.21 indexed citations
Dash, Denver & Gregory F. Cooper. (2002). Exact model averaging with naive Bayesian classifiers. International Conference on Machine Learning. 91–98.24 indexed citations
18.
Dash, Denver & Marek J. Drużdżel. (2002). Robust independence testing for constraint-based learning of causal structure. 167–174.31 indexed citations
19.
Wang, Haiqin, Denver Dash, & Marek J. Drużdżel. (2001). A Method for Evaluating Elicitation Schemes for Probabilities. The Florida AI Research Society. 607–612.1 indexed citations
20.
Dash, Denver & Marek J. Drużdżel. (1999). A hybrid anytime algorithm for the construction of causal models from sparse data. Uncertainty in Artificial Intelligence. 142–149.60 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.