Daniel L. Sussman
- Molecular Biology
- Statistical and Nonlinear Physics top 5%
- Cellular and Molecular Neuroscience top 10%
- Artificial Intelligence top 10%
- Genetics
- Co-authors
- Silvana B. RossoAnthony Wynshaw‐BorisPatricia C. SalinasCarey E. PriebeMinh TangDonniell E. FishkindVince LyzinskiAvanti Athreya
- Topics
- Complex Network Analysis Techniques (9 papers)Advanced Graph Neural Networks (6 papers)Bayesian Methods and Mixture Models (3 papers)
- Cited by
- Statistical and Nonlinear PhysicsDevelopmental NeuroscienceCellular and Molecular Neuroscience
- Journals
- Journal of the American Statistical AssociationNature NeuroscienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Daniel L. Sussman
24 papers receiving 872 citations
Peers
Comparison fields: 5 of 117
- Molecular Biology 391
- Statistical and Nonlinear Physics 233
- Cellular and Molecular Neuroscience 180
- Artificial Intelligence 164
- Genetics 111
Countries citing papers authored by Daniel L. Sussman
This map shows the geographic impact of Daniel L. Sussman'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 L. Sussman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel L. Sussman more than expected).
Fields of papers citing papers by Daniel L. Sussman
This network shows the impact of papers produced by Daniel L. Sussman. 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 L. Sussman. The network helps show where Daniel L. Sussman may publish in the future.
Co-authorship network of co-authors of Daniel L. Sussman
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel L. Sussman. A scholar is included among the top collaborators of Daniel L. Sussman 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 L. Sussman. Daniel L. Sussman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 17 | |
| 7 | Statistical inference on random dot product graphs: a survey | 47 |
| 8 | 33 | |
| 9 | 41 | |
| 10 | A limit theorem for scaled eigenvectors of random dot product graphs | 3 |
| 11 | 46 | |
| 12 | 5 | |
| 13 | 117 | |
| 14 | 21 | |
| 15 | A consistent dot product embedding for stochastic blockmodel graphs | 5 |
| 16 | 20 | |
| 17 | 5 | |
| 18 | 396 | |
| 19 | 4 | |
| 20 | 39 |
About Daniel L. Sussman
Daniel L. Sussman is a scholar working on Space and Planetary Science, Statistical and Nonlinear Physics and Statistics and Probability, having authored 24 papers that have together received 888 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (9 papers), Advanced Graph Neural Networks (6 papers) and Bayesian Methods and Mixture Models (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (233 citations), Developmental Neuroscience (55 citations) and Cellular and Molecular Neuroscience (180 citations). Daniel L. Sussman has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Silvana B. Rosso, Anthony Wynshaw‐Boris, Patricia C. Salinas, Carey E. Priebe, Minh Tang, Donniell E. Fishkind, Vince Lyzinski, Avanti Athreya, Ronald M. Summers and Jianhua Yao. Their work appears in journals such as Journal of the American Statistical Association, Nature Neuroscience and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.