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.
Concentration of Measure for the Analysis of Randomized Algorithms
Countries citing papers authored by Devdatt Dubhashi
Since
Specialization
Citations
This map shows the geographic impact of Devdatt Dubhashi'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 Devdatt Dubhashi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Devdatt Dubhashi more than expected).
Fields of papers citing papers by Devdatt Dubhashi
This network shows the impact of papers produced by Devdatt Dubhashi. 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 Devdatt Dubhashi. The network helps show where Devdatt Dubhashi may publish in the future.
Co-authorship network of co-authors of Devdatt Dubhashi
This figure shows the co-authorship network connecting the top 25 collaborators of Devdatt Dubhashi.
A scholar is included among the top collaborators of Devdatt Dubhashi 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 Devdatt Dubhashi. Devdatt Dubhashi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Panahi, Ashkan, Devdatt Dubhashi, Fredrik Johansson, & Chiranjib Bhattacharyya. (2017). Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery. Chalmers Research (Chalmers University of Technology). 70. 2769–2777.14 indexed citations
8.
Mogren, Olof, Mikael Kågebäck, & Devdatt Dubhashi. (2015). Extractive summarization by aggregating multiple similarities. Chalmers Publication Library (Chalmers University of Technology). 2015. 451–457.10 indexed citations
9.
Johansson, Fredrik, et al.. (2014). Global graph kernels using geometric embeddings. Chalmers Research (Chalmers University of Technology). 694–702.25 indexed citations
10.
Martinsson, Anders, et al.. (2013). Lovász ϑ function, SVMs and finding dense subgraphs. Journal of Machine Learning Research. 14(1). 3495–3536.4 indexed citations
11.
Martinsson, Anders, et al.. (2012). The Lovász ϑ function, SVMs and finding large dense subgraphs. Neural Information Processing Systems. 25. 1160–1168.4 indexed citations
Clermont, Gilles, Charles Auffray, Yves Moreau, et al.. (2009). Translating systems biology into medical applications : Report of the 3rd Bertinoro Systems Biology Workshop. Genome biology.2 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.