Daniel Smilkov

2.1k total citations
10 papers, 533 citations indexed

About

Daniel Smilkov is a scholar working on Statistical and Nonlinear Physics, Geometry and Topology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Daniel Smilkov has authored 10 papers receiving a total of 533 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Statistical and Nonlinear Physics, 2 papers in Geometry and Topology and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Daniel Smilkov's work include Complex Network Analysis Techniques (6 papers), Opinion Dynamics and Social Influence (4 papers) and COVID-19 epidemiological studies (2 papers). Daniel Smilkov is often cited by papers focused on Complex Network Analysis Techniques (6 papers), Opinion Dynamics and Social Influence (4 papers) and COVID-19 epidemiological studies (2 papers). Daniel Smilkov collaborates with scholars based in North Macedonia, United States and Italy. Daniel Smilkov's co-authors include Fernanda Viégas, Martin Wattenberg, Ljupčo Kocarev, James Wexler, Dilip Krishnan, Kanit Wongsuphasawat, Martin C. Stumpe, Greg S. Corrado, Narayan Hegde and Emily Reif and has published in prestigious journals such as Scientific Reports, Physica A Statistical Mechanics and its Applications and IEEE Transactions on Visualization and Computer Graphics.

In The Last Decade

Daniel Smilkov

10 papers receiving 512 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel Smilkov North Macedonia	6	253	145	69	65	52	10	533
Dennis Wei United States	18	493 1.9×	95 0.7×	88 1.3×	14 0.2×	227 4.4×	54	867
Josua Krause United States	5	335 1.3×	241 1.7×	30 0.4×	12 0.2×	34 0.7×	7	494
James Wexler United States	7	409 1.6×	211 1.5×	54 0.8×	7 0.1×	87 1.7×	13	641
Peng Qi China	9	486 1.9×	89 0.6×	51 0.7×	7 0.1×	63 1.2×	33	692
Anagha Kulkarni United States	14	331 1.3×	111 0.8×	11 0.2×	16 0.2×	41 0.8×	54	627
Sungchang Lee South Korea	12	196 0.8×	123 0.8×	12 0.2×	29 0.4×	16 0.3×	44	519
NhatHai Phan United States	12	461 1.8×	121 0.8×	10 0.1×	35 0.5×	15 0.3×	37	661
Jana Nowaková Czechia	8	146 0.6×	47 0.3×	24 0.3×	7 0.1×	18 0.3×	26	327
Loredana Caruccio Italy	13	287 1.1×	72 0.5×	43 0.6×	13 0.2×	6 0.1×	36	511

Countries citing papers authored by Daniel Smilkov

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Smilkov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Smilkov

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Smilkov. A scholar is included among the top collaborators of Daniel Smilkov 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 Daniel Smilkov. Daniel Smilkov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Smilkov, Daniel, Nikhil Thorat, Ann Yuan, et al.. (2019). TensorFlow.js: Machine Learning for the Web and Beyond. arXiv (Cornell University). 1. 309–321. 5 indexed citations

2.

Cai, Carrie J., Emily Reif, Narayan Hegde, et al.. (2019). Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. 1–14. 222 indexed citations

3.

Wongsuphasawat, Kanit, Daniel Smilkov, James Wexler, et al.. (2017). Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. IEEE Transactions on Visualization and Computer Graphics. 24(1). 1–12. 222 indexed citations

4.

Smilkov, Daniel, César A. Hidalgo, & Ljupčo Kocarev. (2014). Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics. Scientific Reports. 4(1). 4795–4795. 29 indexed citations

5.

Smilkov, Daniel & Ljupčo Kocarev. (2012). Influence of the network topology on epidemic spreading. Physical Review E. 85(1). 14 indexed citations

6.

Stanoev, Angel, Daniel Smilkov, & Ljupčo Kocarev. (2011). Identifying communities by influence dynamics in social networks. Physical Review E. 84(4). 46102–46102. 26 indexed citations

7.

Smilkov, Daniel & Ljupčo Kocarev. (2011). Analytically solvable processes on networks. Physical Review E. 84(1). 16104–16104. 1 indexed citations

8.

Smilkov, Daniel & Ljupčo Kocarev. (2010). Rich-club and page-club coefficients for directed graphs. Physica A Statistical Mechanics and its Applications. 389(11). 2290–2299. 8 indexed citations

9.

Smilkov, Daniel, et al.. (2010). Non-intrusive Adaptive Multi-media Routing in Peer-to-Peer Multi-party Video Conferencing. 0. 105–112. 3 indexed citations

10.

Smilkov, Daniel, et al.. (2010). Vulnerability of labeled networks. Physica A Statistical Mechanics and its Applications. 389(23). 5538–5549. 3 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.

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