| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 84 | 354 | 631 | 46 |
IEEE Transactions on Computational Social Systems investigates areas of study like Artificial intelligence, Social network, Data modeling, Data science and Social media. It explores issues in Artificial intelligence which can be linked to other research areas like Machine learning, Task analysis and Pattern recognition. The Social media study tackling the subject of Microblogging is the focus of it.
The journal publications focus on Computer security, Data science, Data mining, The Internet and Social media. While work presented in the journal publications provide substantial information on Data mining, it also covers topics in Recommender system, Markov chain and Social network. The most cited publications about Microblogging research are fields of study within Social media but they also intertwine with concepts in Social environment.
The journal is mainly concerned with subjects like Artificial intelligence, Data modeling, Machine learning, Data science and Social network. In addition to Artificial intelligence research, IEEE Transactions on Computational Social Systems aims to explore topics under Pattern recognition, Task analysis and Natural language processing. Recommender system is a focus of the presented Machine learning works and it dives deep in Recommender system.
The study on Social network presented in it intersects with subjects under the field of The Internet. Feature extraction study tackled is connected to the field of Feature (machine learning).
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 IEEE Transactions on Computational Social Systems (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 IEEE Transactions on Computational Social Systems (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, 21.86% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 27.38% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.74% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.24% of all publications and 44.64% 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.
The research topics studied at IEEE Transactions on Computational Social Systems, such as Artificial Intelligence, Social Network, and Data Modeling, not only have significant scientific and technical implications but also hold promising career opportunities. For instance, a firm grasp of these themes enables one to establish a successful career as a data scientist or a computational social systems analyst. Moreover, a deep understanding of these subjects also allows one to take a slightly different career path, like teaching. Converting intricate technical knowledge into an understandable format for learners requires both profound expertise in the subject and a knack for effective communication. Combine these skills, and you could have a successful career in education. Consider, for instance, becoming a math teacher for middle school students. If you're interested in teaching, especially in unique and challenging environments like Alaska, you may want to explore this opportunity further. Here is a guide on how to be a middle school math teacher in alaska to help start your journey. Remember, with the ever-growing reliance on data and technology, the subjects studied and discussed at IEEE Transactions on Computational Social Systems are increasingly relevant. Whether you aim to apply these principles directly in industries or pass on the knowledge to the next generation, knowing these topics will offer many opportunities for professional growth and personal satisfaction.
Lifang Li;Qingpeng Zhang;Xiao Wang;Jun Zhang
(2020)Xiaokang Zhou;Wei Liang;Kevin I-Kai Wang;Laurence T. Yang
(2021)Pawan Kumar Verma;Prateek Agrawal;Ivone Amorim;Radu Prodan
(2021)Fan Wang;Haibin Zhu;Gautam Srivastava;Shancang Li
(2021)Sudhanshu Kumar;Kanjar De;Partha Pratim Roy
(2020)Haoyue Liu;Ishani Chatterjee;MengChu Zhou;Xiaoyu Sean Lu
(2020)Gulshan Shrivastava;Prabhat Kumar;Rudra Pratap Ojha;Pramod Kumar Srivastava
(2020)Laisen Nie;Yixuan Wu;Xiaojie Wang;Lei Guo
(2021)Pursuing a Computer Science degree in the USA opens up various educational and career opportunities. For those interested in advancing their expertise, exploring easy masters degrees can offer a streamlined path to higher qualifications without overwhelming complexity.
Many students seek cost-effective routes for advanced studies. Affordable options such as the affordable doctoral programs available online make it easier to continue education while managing expenses.
For undergraduates or those starting their degree journey, selecting institutions recognized for financial aid is crucial. Students should consider enrolling in FAFSA-approved online colleges to maximize funding opportunities and reduce tuition costs.
In addition to degrees, short-term skill enhancement is possible through online courses with certificates. These certifications often focus on in-demand skills and can boost employability in tech-driven roles.
