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.
An improved data stream summary: the count-min sketch and its applications
Countries citing papers authored by Graham Cormode
Since
Specialization
Citations
This map shows the geographic impact of Graham Cormode'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 Graham Cormode with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Graham Cormode more than expected).
This network shows the impact of papers produced by Graham Cormode. 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 Graham Cormode. The network helps show where Graham Cormode may publish in the future.
Co-authorship network of co-authors of Graham Cormode
This figure shows the co-authorship network connecting the top 25 collaborators of Graham Cormode.
A scholar is included among the top collaborators of Graham Cormode 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 Graham Cormode. Graham Cormode is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hickey, Chris & Graham Cormode. (2018). Cheap checking for cloud computing : statistical analysis via annotated data streams. International Conference on Artificial Intelligence and Statistics. 1318–1326.
9.
Cormode, Graham, et al.. (2018). Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms. Warwick Research Archive Portal (University of Warwick). 1048–1056.1 indexed citations
10.
Zhang, Jun, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, & Xiaokui Xiao. (2017). PrivBayes. ACM Transactions on Database Systems. 42(4). 1–41.212 indexed citations
11.
Chakrabarti, Amit, Graham Cormode, Andrew McGregor, Justin Thaler, & Suresh Venkatasubramanian. (2013). On Interactivity in Arthur-Merlin Communication and Stream Computation.. Electronic colloquium on computational complexity. 20. 180.2 indexed citations
12.
Cormode, Graham, S. Muthukrishnan, & Ke Yi. (2012). Large-Scale Distributed Computation (NII Shonan Meeting 2012-1).. 2012.1 indexed citations
13.
Bhagat, Smriti, Graham Cormode, Balachander Krishnamurthy, & Divesh Srivastava. (2010). Prediction promotes privacy in dynamic social networks. 6–6.20 indexed citations
Abello, James & Graham Cormode. (2006). Discrete Methods in Epidemiology: Dimacs Workshop, Date Mining And Epidemioloogy, March 18-19, 2004, Dimacs Center, Rutgers University (Dimacs Series in ... and Theoretical Computer Science). American Mathematical Society eBooks.4 indexed citations
Cormode, Graham, et al.. (2005). Summarizing and mining inverse distributions on data streams via dynamic inverse sampling. Very Large Data Bases. 25–36.49 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.