Nicole Immorlica

97 papers receiving 3.8k citations

Hit Papers

Locality-sensitive hashing scheme based on p-stable distr...2004202620112018200450010001.5k

Peers

Nicole Immorlica
Comparison fields: 5 of 127
  • Computer Vision and Pattern Recognition 1.5k
  • Management Science and Operations Research 1.1k
  • Computer Networks and Communications 989
  • Artificial Intelligence 866
  • Signal Processing 444
Replace Vahab Mirrokni with:
Vahab Mirrokni United States
Yevgeniy Dodis United States
Xiaotie Deng China
Shuchi Chawla United States
Amos Fiat Israel
Alexander Felfernig Austria
Neel Sundaresan United States
Gerald Tesauro United States
Filip Radlinski United States
Defu Lian China
Nicole Immorlica relative to Vahab Mirrokni United States Vahab Mirrokni's profile →
Citations per field
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Vahab Mirrokni · 1×
Citations per year

Countries citing papers authored by Nicole Immorlica

Since Specialization
Citations

This map shows the geographic impact of Nicole Immorlica'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 Nicole Immorlica with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicole Immorlica more than expected).

Fields of papers citing papers by Nicole Immorlica

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicole Immorlica. 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 Nicole Immorlica. The network helps show where Nicole Immorlica may publish in the future.

Co-authorship network of co-authors of Nicole Immorlica

This figure shows the co-authorship network connecting the top 25 collaborators of Nicole Immorlica. A scholar is included among the top collaborators of Nicole Immorlica 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 Nicole Immorlica. Nicole Immorlica 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
#WorkIndexed citations
1 2
2 1
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Incentivizing Exploration with Unbiased Histories
3
4
When optimizing nonlinear objectives is no harder than linear objectives.
2
5
Influence Maximization with an Unknown Network by Exploiting Community Structure.
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6
The Importance of Communities for Learning to Influence
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7 24
8 6
9 7
10
On the Impossibility of Black-Box Transformations in Mechanism Design
1
11 10
12
Emergence of Cooperation in ANonymous Social Networks through Social Capital
3
13 37
14
Game-Theoretic Aspects of Designing Hyperlink Structures
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15 83
16
Bid Optimization in Online Advertisement Auctions
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17 89
18 29
19 111
20 61

About Nicole Immorlica

Nicole Immorlica is a scholar working on Management Science and Operations Research, Marketing and Safety Research, having authored 99 papers that have together received 3.9k indexed citations. Recurring topics across this work include Auction Theory and Applications (49 papers), Game Theory and Applications (27 papers) and Consumer Market Behavior and Pricing (18 papers). The work is most often cited by research in Management Science and Operations Research (1.1k citations), Computer Vision and Pattern Recognition (1.5k citations) and Computer Networks and Communications (989 citations). Nicole Immorlica has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Vahab Mirrokni, Mayur Datar, Piotr Indyk, Mohammad Mahdian, Moshe Babaioff, Robert Kleinberg, Steve Chien, Brendan Lucier, Jennifer Chayes and Christian Borgs. Their work appears in journals such as Management Science, Operations Research and Journal of the ACM.

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.

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