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.
Countries citing papers authored by David C. Parkes
Since
Specialization
Citations
This map shows the geographic impact of David C. Parkes'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 David C. Parkes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David C. Parkes more than expected).
This network shows the impact of papers produced by David C. Parkes. 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 David C. Parkes. The network helps show where David C. Parkes may publish in the future.
Co-authorship network of co-authors of David C. Parkes
This figure shows the co-authorship network connecting the top 25 collaborators of David C. Parkes.
A scholar is included among the top collaborators of David C. Parkes 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 David C. Parkes. David C. Parkes 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.
Palacios‐Huerta, Ignacio, David C. Parkes, & Richard Steinberg. (2024). Combinatorial Auctions in Practice. Journal of Economic Literature. 62(2). 517–553.2 indexed citations
Parkes, David C., et al.. (2020). From Predictions to Decisions: Using Lookahead Regularization. Neural Information Processing Systems. 33. 4115–4126.1 indexed citations
10.
Feng, Zhe, et al.. (2019). Optimal Auctions through Deep Learning. International Conference on Machine Learning. 1706–1715.37 indexed citations
11.
Abebe, Rediet, Jon Kleinberg, & David C. Parkes. (2017). Fair Division via Social Comparison. arXiv (Cornell University). 281–289.24 indexed citations
Toulis, Panos & David C. Parkes. (2015). Statistical inference of long-term causal effects in multiagent systems under the Neyman-Rubin model.. arXiv (Cornell University).
14.
Chen, William, et al.. (2013). Generalized Method-of-Moments for Rank Aggregation. Neural Information Processing Systems. 26. 2706–2714.36 indexed citations
15.
Zhang, Haoqi, Eric Horvitz, Yiling Chen, & David C. Parkes. (2012). Task routing for prediction tasks. Adaptive Agents and Multi-Agents Systems. 889–896.24 indexed citations
Hubaux, Jean‐Pierre, et al.. (2010). Security Games in Online Advertising: Can Ads Help Secure the Web?. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 47(5). 193–6.7 indexed citations
18.
Parkes, David C., et al.. (2004). HarTAC– The Harvard TAC SCM'03 Agent. Digital Access to Scholarship at Harvard (DASH) (Harvard University).5 indexed citations
19.
Parkes, David C.. (2001). An Iterative Generalized Vickrey Auction: Strategy-Proofness without Complete Revelation. Digital Access to Scholarship at Harvard (DASH) (Harvard University).26 indexed citations
20.
Parkes, David C. & Lyle Ungar. (2000). Preventing Strategic Manipulation in Iterative Auctions: Proxy Agents and Price-Adjustment. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 82–89.66 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.