Colin Sandon

621 total citations
11 papers, 211 citations indexed

About

Colin Sandon is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Colin Sandon has authored 11 papers receiving a total of 211 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Statistical and Nonlinear Physics, 5 papers in Artificial Intelligence and 4 papers in Computer Networks and Communications. Recurrent topics in Colin Sandon's work include Complex Network Analysis Techniques (6 papers), Random Matrices and Applications (4 papers) and Opinion Dynamics and Social Influence (3 papers). Colin Sandon is often cited by papers focused on Complex Network Analysis Techniques (6 papers), Random Matrices and Applications (4 papers) and Opinion Dynamics and Social Influence (3 papers). Colin Sandon collaborates with scholars based in United States, Switzerland and Bulgaria. Colin Sandon's co-authors include Emmanuel Abbé, Fabrizio Zanello, Elchanan Mossel, Noga Alon and Afonso S. Bandeira and has published in prestigious journals such as IEEE Transactions on Information Theory, Communications on Pure and Applied Mathematics and Journal of Combinatorial Theory Series A.

In The Last Decade

Colin Sandon

11 papers receiving 205 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Colin Sandon United States	7	112	97	59	33	30	11	211
Joe Neeman United States	6	56 0.5×	61 0.6×	43 0.7×	22 0.7×	23 0.8×	21	152
Marianna Bolla Hungary	9	72 0.6×	51 0.5×	42 0.7×	16 0.5×	48 1.6×	30	213
Jop Briët Netherlands	8	49 0.4×	151 1.6×	15 0.3×	15 0.5×	77 2.6×	28	248
Tselil Schramm United States	7	28 0.3×	69 0.7×	41 0.7×	20 0.6×	38 1.3×	20	143
J. Inglis United Kingdom	7	103 0.9×	50 0.5×	21 0.4×	41 1.2×	41 1.4×	12	259
Agnes Radl Germany	8	72 0.6×	61 0.6×	15 0.3×	12 0.4×	69 2.3×	15	218
Debarghya Ghoshdastidar India	7	69 0.6×	60 0.6×	13 0.2×	4 0.1×	20 0.7×	15	160
Samuel B. Hopkins United States	6	16 0.1×	53 0.5×	52 0.9×	13 0.4×	26 0.9×	16	161
V. V. Prelov Russia	8	39 0.3×	47 0.5×	28 0.5×	123 3.7×	23 0.8×	37	261

Countries citing papers authored by Colin Sandon

Since Specialization

Citations

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

Fields of papers citing papers by Colin Sandon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Colin Sandon

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

Abbé, Emmanuel & Colin Sandon. (2023). A proof that Reed-Muller codes achieve Shannon capacity on symmetric channels. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 177–193. 14 indexed citations

Abbé, Emmanuel & Colin Sandon. (2023). Polynomial‐time universality and limitations of deep learning. Communications on Pure and Applied Mathematics. 76(11). 3493–3549. 1 indexed citations

Mossel, Elchanan, et al.. (2023). The Power of Two Matrices in Spectral Algorithms for Community Recovery. IEEE Transactions on Information Theory. 70(5). 3599–3621. 2 indexed citations

Abbé, Emmanuel & Colin Sandon. (2020). On the universality of deep learning. Neural Information Processing Systems. 33. 20061–20072. 1 indexed citations

Abbé, Emmanuel & Colin Sandon. (2017). Proof of the Achievability Conjectures for the General Stochastic Block Model. Communications on Pure and Applied Mathematics. 71(7). 1334–1406. 26 indexed citations

Abbé, Emmanuel & Colin Sandon. (2016). Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation. Neural Information Processing Systems. 29. 1334–1342. 9 indexed citations

Abbé, Emmanuel & Colin Sandon. (2016). Crossing the KS threshold in the stochastic block model with information theory. 840–844. 10 indexed citations

Abbé, Emmanuel, Noga Alon, Afonso S. Bandeira, & Colin Sandon. (2016). Linear Boolean Classification, Coding and the Critical Problem. IEEE Transactions on Information Theory. 62(4). 1667–1673. 2 indexed citations

Abbé, Emmanuel & Colin Sandon. (2015). Recovering communities in the general stochastic block model without knowing the parameters. arXiv (Cornell University). 28. 676–684. 6 indexed citations

10.

Abbé, Emmanuel & Colin Sandon. (2015). Community Detection in General Stochastic Block models: Fundamental Limits and Efficient Algorithms for Recovery. 670–688. 131 indexed citations

11.

Sandon, Colin & Fabrizio Zanello. (2012). Warnaarʼs bijection and colored partition identities, I. Journal of Combinatorial Theory Series A. 120(1). 28–38. 9 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.

Explore authors with similar magnitude of impact