| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 305 | 98 | 112 | 19 |
The main research concerns discussed in Social Network Analysis and Mining are Social network, Social media, Artificial intelligence, World Wide Web and Data mining. Social network analysis is a major topic of Social network research. It addresses concerns in Social media which are intertwined with other disciplines, such as Sentiment analysis, Information retrieval and Data science.
Topics in Artificial intelligence were tackled in line with various other fields like Machine learning and Natural language processing. Recommender system is part of World Wide Web studies tackled in it. The journal explores issues in Data mining which can be linked to other research areas like Cluster analysis and Complex network.
Some problems in Theoretical computer science that were presented in it overlapped with concepts under Node (networking), Centrality and Graph (abstract data type). Centrality research is the primary subject tackled in the journal with a focus on Betweenness centrality.
The journal publications mostly deal with topics like Social network, Social media, Data science, World Wide Web and Artificial intelligence. The most cited papers focus on Social network but the discussions also offer insight into other areas such as Context (language use), Data mining, Identification (information), Cluster analysis and Dynamic network analysis. While Social media is the key highlight in the most cited publications, thet also covered some subjects on Sentiment analysis and Naive Bayes classifier.
The journal explores disciplines such as Social media, Artificial intelligence, Node (networking), Social network and Theoretical computer science. In the journal, Sentiment analysis, Social network analysis, Data science and Internet privacy are investigated in conjunction with one another to address concerns in Social media research. The studies on Artificial intelligence discussed can also contribute to research in the domains of Machine learning and Natural language processing.
The journal explores topics in Node (networking) which can be helpful for research in disciplines like Network model, Data mining, Degree (graph theory), Network science and Eigenvalues and eigenvectors. The research on Social network tackled can also make contributions to studies in the areas of Graph (abstract data type), Probabilistic logic and Phenomenon. The concepts on Theoretical computer science presented in it can also apply to other research fields, including Betweenness centrality, Centrality, Link (geometry) and Complex network.
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 Social Network Analysis and Mining (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 Social Network Analysis and Mining (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, 10.20% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 4.55% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.68% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 10.23% of all publications and 79.55% 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.
A career in Social Network Analysis and Mining can offer interesting and diverse opportunities. This field provides numerous possibilities to specialize in various research areas such as Artificial intelligence, Data mining, Sentiment analysis, and others mentioned in the above article. Depending upon your area of interest, there are several career options to consider. For instance, a role as a Social Network Analyst or researchers specializing in these topics. In addition to a rewarding and diverse career, the remuneration in this field can also be attractive. For students or young professionals considering an entry-level position in a related field, like a preschool teacher assistant, the salaries can be enticing. For instance, the{preschool teacher assistant salary in florida} can give you an idea about the financial aspects of choosing such a career pathway. Besides, ongoing research contributions, like the ones cited in this journal, show the dynamic nature of the field. Furthermore, the new topics explored in the last edition indicate the potential growth and developments in the field. Therefore, students and professionals should keep themselves updated about the latest developments in this area to enhance their career prospects. Investing time in gaining theoretical and practical knowledge in Social Network Analysis and Mining can open up various opportunities. Therefore, budding professionals should aim to contribute to this field through their research while also keeping an eye on the upcoming trends.
Rafiqul Islam;Shaowu Liu;Xianzhi Wang;Guandong Xu
(2020)Abubakr H. Ombabi;Wael Ouarda;Adel M. Alimi
(2020)Reza Motamedi;Soheil Jamshidi;Reza Rejaie;Walter Willinger
(2020)Hasib Zunair;A. Ben Hamza
(2021)Derek Weber;Frank Neumann
(2021)Pursuing a Computer Science degree in the USA opens up diverse educational paths, catering to various career goals and budgets. For those considering advanced studies, exploring easiest masters degrees can help identify programs that balance academic rigor with manageable coursework, making it easier to upskill without overwhelming commitments.
Students seeking the highest level of education may look into the cheapest online phd programs, which provide a cost-effective way to achieve a doctoral degree while continuing work or research remotely.
Affordability is a crucial factor for many, and finding cheapest online colleges that accept fafsa in usa ensures access to financial aid options, making online education more accessible without sacrificing quality.
Additionally, acquiring specialized skills through certificates online can boost employability and salary prospects in competitive tech fields, often requiring less time and money than traditional degrees.
