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 Nir Friedman'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 Nir Friedman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nir Friedman more than expected).
This network shows the impact of papers produced by Nir Friedman. 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 Nir Friedman. The network helps show where Nir Friedman may publish in the future.
Co-authorship network of co-authors of Nir Friedman
This figure shows the co-authorship network connecting the top 25 collaborators of Nir Friedman.
A scholar is included among the top collaborators of Nir Friedman 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 Nir Friedman. Nir Friedman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lara‐Astiaso, David, Assaf Weiner, Erika Lorenzo-Vivas, et al.. (2014). Chromatin state dynamics during blood formation. Science. 345(6199). 943–949.556 indexed citations breakdown →
5.
Regev, Aviv, Gloria A. Brar, Moran Yassour, et al.. (2011). High-Resolution View of the Yeast Meiotic Program Revealed by Ribosome Profiling. DSpace@MIT (Massachusetts Institute of Technology).6 indexed citations
6.
El‐Hay, Tal, et al.. (2010). Continuous-time belief propagation. International Conference on Machine Learning. 41(2). 343–350.16 indexed citations
7.
Koller, Daphne & Nir Friedman. (2009). Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press eBooks.296 indexed citations
Getoor, Lise, et al.. (2001). Learning Probabilistic Models of Relational Structure. International Conference on Machine Learning. 170–177.118 indexed citations
17.
Elidan, Gal, et al.. (2000). Discovering Hidden Variables: A Structure-Based Approach. Neural Information Processing Systems. 13. 479–485.58 indexed citations
Friedman, Nir & Lise Getoor. (1999). Efficient learning using constrained sufficient statistics.. International Conference on Artificial Intelligence and Statistics.4 indexed citations
20.
Boutilier, Craig, Nir Friedman, & Joseph Y. Halpern. (1998). Belief revision with unreliable observations. National Conference on Artificial Intelligence. 127–134.22 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.