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 Nataša Milić-Frayling
Since
Specialization
Citations
This map shows the geographic impact of Nataša Milić-Frayling'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 Nataša Milić-Frayling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nataša Milić-Frayling more than expected).
Fields of papers citing papers by Nataša Milić-Frayling
This network shows the impact of papers produced by Nataša Milić-Frayling. 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 Nataša Milić-Frayling. The network helps show where Nataša Milić-Frayling may publish in the future.
Co-authorship network of co-authors of Nataša Milić-Frayling
This figure shows the co-authorship network connecting the top 25 collaborators of Nataša Milić-Frayling.
A scholar is included among the top collaborators of Nataša Milić-Frayling 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 Nataša Milić-Frayling. Nataša Milić-Frayling is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Raza, Mohammad, Sumit Gulwani, & Nataša Milić-Frayling. (2015). Compositional program synthesis from natural language and examples. International Conference on Artificial Intelligence. 792–800.26 indexed citations
3.
Rodrigues, Eduarda Mendes, et al.. (2013). Network analysis of third party tracking: user exposure to tracking cookies through search. ePrints Soton (University of Southampton).1 indexed citations
Kazai, Gabriella & Nataša Milić-Frayling. (2009). On the Evaluation of the Quality of Relevance Assessments Collected through Crowdsourcing.13 indexed citations
Kantor, Paul B., Gabriella Kazai, Nataša Milić-Frayling, & Ross Wilkinson. (2008). Proceedings of the 2008 ACM workshop on Research advances in large digital book repositories.1 indexed citations
Leskovec, Jure, Nataša Milić-Frayling, & Marko Grobelnik. (2005). Impact of linguistic analysis on the semantic graph coverage and learning of document extracts. National Conference on Artificial Intelligence. 1069–1074.27 indexed citations
Brank, Janez, Marko Grobelnik, Nataša Milić-Frayling, & Dunja Mladenić. (2003). Sparsity analysis of term weighting schemes: Application to Feature Selection. 10.1 indexed citations
18.
Zhai, ChengXiang, et al.. (1996). Experiments on chinese text indexing : CLARIT TREC-5 Chinese track report. Text REtrieval Conference. 335–339.5 indexed citations
19.
Milić-Frayling, Nataša, et al.. (1996). CLARIT compound queries and constraint-controlled feedback in TREC-5 Ad-Hoc experiments. Text REtrieval Conference. 315–334.2 indexed citations
20.
Zhai, ChengXiang, et al.. (1996). OCR correction and query expansion for retrieval on OCR data : CLARIT TREC-5 confusion track report. Text REtrieval Conference. 341–345.11 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.