| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 215 | 32 | 74 | 12 |
| Computer Science | 928 | 8 | 9 | 4 |
Journal of Complexity facilitates discussions on Discrete mathematics, Combinatorics, Applied mathematics, Mathematical analysis and Function (mathematics). The research on Discrete mathematics featured in the journal combines topics in other fields like Hilbert space, Sobolev space, Polynomial, Algebra and Bounded function. In addition to Combinatorics research, it aims to explore topics under Class (set theory), Upper and lower bounds, Order (group theory) and Sequence.
The work on Applied mathematics addressed in Journal of Complexity expands to the thematically related Mathematical optimization. The Mathematical optimization study tackled is a key component of adjacent topics in the area of Algorithm. The studies tackled, which mainly focus on Mathematical analysis, apply to Pure mathematics as well.
The most cited papers are organized to reinforce research efforts on Discrete mathematics, Combinatorics, Algorithm, Applied mathematics and Mathematical analysis. The journal publications focus on Discrete mathematics but the discussions also offer insight into other areas such as Bounded function, Sobolev space, Function (mathematics), Upper and lower bounds and Polynomial. While the most cited articles focused on Combinatorics, they were also able to explore topics like Zero (complex analysis), Measure (mathematics), Simple (abstract algebra) and Multivariate statistics.
The main research concerns discussed in Journal of Complexity are Applied mathematics, Combinatorics, Function (mathematics), Discrete mathematics and Upper and lower bounds. Topics in Applied mathematics explored in it were investigated in conjunction with research in Kernel (statistics), Smoothness (probability theory), Estimator, Quantile regression and Lipschitz continuity. Combinatorics research presented in Journal of Complexity encompasses a variety of subjects, including Bounded function, Linear subspace, Multiplier (Fourier analysis) and Sobolev space.
Function (mathematics) research featured in Journal of Complexity incorporates concerns from various other topics such as Matching (graph theory), Convolution and Pure mathematics. Function space is part of Discrete mathematics studies tackled in it. Journal of Complexity addresses concerns in Upper and lower bounds which are intertwined with other disciplines, such as Statistical learning theory, Unit square, Point set and Fibonacci number.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Journal of Complexity (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Journal of Complexity (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 9.43% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 31.25% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.58% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 10.42% of all publications and 43.75% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Vladimir N. Temlyakov
(2021)Ioannis K. Argyros;Santhosh George
(2020)Patrick Cheridito;Arnulf Jentzen;Florian Rossmannek
(2021)Arnulf Jentzen;Philippe von Wurstemberger
(2020)Matthieu Dolbeault;Albert Cohen
(2022)Zhiying Fang;Zheng-Chu Guo;Ding-Xuan Zhou
(2020)Vladimir N. Temlyakov;Tino Ullrich
(2021)Stefania Bellavia;Gianmarco Gurioli;Benedetta Morini;Philippe Toint
(2022)For students exploring Computer Science in the USA, online education offers flexible and affordable pathways. Many learners opt for accredited self-paced online colleges, which allow them to progress through coursework at their own speed while maintaining recognized academic standards.
Cost is another important consideration. Programs categorized under the least expensive online masters provide valuable advanced training without the heavy financial burden, making them ideal for career changers or professionals seeking growth in tech roles.
For those just starting out, associate degrees can be a practical entry point. Resources highlighting the easiest associate degree to get can help students identify foundational programs that build essential skills with reasonable time commitments.
Choosing a program from accredited online colleges ensures quality education and recognition across the industry, which is crucial when pursuing competitive career pathways in Computer Science.