| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 470 | 26 | 31 | 13 |
The aim of ACM Journal of Experimental Algorithms is to expand the discussion of research in Algorithm, Theoretical computer science, Discrete mathematics, Mathematical optimization and Combinatorics. While the journal focused on Algorithm, it was also able to explore topics like Graph (abstract data type), Set (abstract data type), Speedup, Parallel computing and Data structure. The Speedup study featured in it draws connections with the study of Dijkstra's algorithm.
The concepts on Parallel computing presented in it can also apply to other research fields, including Sorting, Quicksort, Integer sorting and External sorting. Topics in Theoretical computer science explored in ACM Journal of Experimental Algorithms were investigated in conjunction with research in Node (networking) and Matching (graph theory). The featured works in Graph, which all belong in the domain if Discrete mathematics, also overlaps with concepts under Bounded function.
The journal tackles research in Heuristics as part of the general discipline of Mathematical optimization, however, it also discusses concepts in Context (language use). It dives deep in exploring the relationship between the study of Heuristics and Heuristic. Approximation algorithm and Time complexity are some of the facets of Combinatorics tackled in the journal.
The journal publications mainly deal with areas of study such as Algorithm, Theoretical computer science, Combinatorics, Shortest Path Faster Algorithm and Suurballe's algorithm. While the published papers focused on Algorithm, they were also able to explore topics like Data structure, Heuristics, String searching algorithm and Graph. While work presented in the most cited publications provide substantial information on Theoretical computer science, it also covers topics in Graph partition, Cluster analysis, Sorting, Path (graph theory) and Speedup.
The concepts of Algorithm, Theoretical computer science, Feedback arc set, Set (abstract data type) and Quantum annealing are tackled in ACM Journal of Experimental Algorithms. The Algorithm study presented in ACM Journal of Experimental Algorithms encompasses related topics like Combinatorial optimization and also examines its connection to subjects such as Noise (video). Some problems in Combinatorial optimization that were presented in ACM Journal of Experimental Algorithms overlapped with concepts under Local search (optimization), Graph (abstract data type), Permutation matrix and Rule-based system.
While work presented in ACM Journal of Experimental Algorithms provided substantial information on Theoretical computer science, it also covered topics in Software and Benchmark (computing). In addition to Set (abstract data type) research, it aims to explore topics under Heuristic, Mathematical proof and Approximation algorithm, Combinatorics. Many of the research works in Data structure, specifically Suffix tree, closely connected to disciplines like Adversary model.
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 ACM Journal of Experimental Algorithms (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 ACM Journal of Experimental Algorithms (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, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 46.15% were posted by at least one author from the top 10 institutions publishing in the journal. Another 15.38% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 23.08% of all publications and 15.38% 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.
It’s noteworthy that careers in the field of Experimental Algorithms are gaining traction worldwide, particularly with the growth of data science and technology advancements. Many of the topics discussed in the ACM Journal of Experimental Algorithms demand expertise in these areas. As such, focusing not only on research but also the practical implications of these studies, it can provide a clearer perspective of how this academic work might contribute to one's professional progression. For example, a role in teaching could be immensely rewarding, allowing you to share your knowledge and inspire future generations in the field. Importantly, for anyone considering a teaching career in Idaho, there are ways to achieve a teaching certificate affordably. You may want to give special attention to understanding how to obtain a [teaching certificate in Idaho](https://research.com/careers/cheapest-way-to-get-a-teaching-credential-in-idaho) that can help you to enter into the education sector of computer science without breaking the bank. In the same vein, building a career in developing robust algorithms can open doors in leading tech firms, startups, and even suffice for establishing your business. Advancements in Algorithm, Theoretical computer science, Discrete mathematics, Mathematical optimization, and Combinatorics can have a significant impact on several industries, providing ample opportunity for meaningful and rewarding work. As we continue to delve into these increasingly complex topics, it's crucial to keep a keen eye on where this knowledge can take us—professionally and personally. Keep in mind, each step you take in the academic journey can be a stepping stone towards a fulfilling career in the field of Experimental Algorithms. So, dive into the research with a clear vision of your professional ambitions, and the journey will become even more rewarding.
Unknown
(2021)Michael Mitzenmacher;Salvatore Pontarelli;Pedro Reviriego
(2020)Unknown
(2021)Tomohiro Koana;Viatcheslav Korenwein;André Nichterlein;Rolf Niedermeier
(2021)Ali Baharev;Hermann Schichl;Arnold Neumaier;Tobias Achterberg
(2021)Michael Jünger;Elisabeth Lobe;Petra Mutzel;Gerhard Reinelt
(2021)Sebastian Schlag;Matthias Schmitt;Christian Schulz
(2021)Wolfgang Fischl;Georg Gottlob;Davide Mario Longo;Reinhard Pichler
(2021)Thomas Bläsius;Tobias Friedrich;Maximilian Katzmann;Anton Krohmer
(2020)Alexandra Henzinger;Alexander Noe;Christian Schulz
(2020)For students pursuing Computer Science in the USA, exploring online degree options can offer flexibility and accessibility. Many reputable options exist, including accredited self paced online colleges that allow learners to progress at their own speed while maintaining quality education standards.
Cost is another key consideration. Programs recognized as cheap masters programs offer affordable pathways to advance your education without compromising on curriculum rigor or accreditation, making them attractive for career growth in tech.
For those starting out or seeking quicker credential options, easy associate degrees in computer-related fields can provide foundational skills and boost employability, leading to entry-level roles or further study.
Choosing the right institution is crucial, and ensuring it is among the online accredited colleges helps guarantee recognized degrees and better career opportunities in competitive tech industries.
