Published by: Springer
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Engineering and Technology | 888 | 18 | 24 | 9 |
The discussions in Applied Network Science mainly cover the fields of Theoretical computer science, Complex network, Node (networking), Centrality and Artificial intelligence. The research on Theoretical computer science tackled can also make contributions to studies in the areas of Graph (abstract data type), Cluster analysis and Graph. The journal emphasizes research on Complex network, which includes concerns such as Network science.
The journal links adjacent topics like Node (networking) with Data mining. The works on Centrality deal in particular with Betweenness centrality. Topics in Artificial intelligence were tackled in line with various other fields like Machine learning and Pattern recognition.
Data science research discussed connects with the study of Social media.
The most cited publications focus largely on the fields of Complex network, Theoretical computer science, Network science, Node (networking) and Network topology. Centrality, Distributed computing, Structure (mathematical logic), Artificial intelligence and Data science are some topics wherein Complex network research discussed in the most cited publications has an impact. Issues in Theoretical computer science were discussed in the published articles, taking into consideration concepts from other disciplines like Measure (mathematics), Clustering coefficient, Cluster analysis and Graph.
Applied Network Science was organized to reinforce research efforts on Node (networking), Centrality, Structure (mathematical logic), Theoretical computer science and Data science. The studies on Node (networking) discussed can also contribute to research in the domains of Data mining and Degree (graph theory). The journal explores topics in Data mining which can be helpful for research in disciplines like Context (language use), Metric (mathematics), Cluster analysis, Feature vector and Cosine similarity.
In addition to Centrality research, Applied Network Science aims to explore topics under Order (exchange), Key (cryptography), Random walk and Identification (information). In it, Quality (business), Machine learning and Artificial intelligence are investigated in conjunction with one another to address concerns in Structure (mathematical logic) research. The studies tackled, which mainly focus on Theoretical computer science, apply to Enhanced Data Rates for GSM Evolution as well.
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 Applied Network Science (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 Applied Network Science (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, 1.28% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 20.78% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.09% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.99% of all publications and 57.14% 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.
Zijing Liu;Mauricio Barahona
(2020)Parul Maheshwari;Réka Albert
(2020)Joseph H. Tien;Marisa C. Eisenberg;Sarah T. Cherng;Mason A. Porter
(2020)Konstantin E. Avrachenkov;Andrei V. Bobu
(2020)R. Maria del Rio-Chanona;Yevgeniya Korniyenko;Manasa Patnam;Mason A. Porter
(2020)John Bollenbacher;Diogo Pacheco;Pik-Mai Hui;Yong-Yeol Ahn
(2021)Danilo Delpini;Stefano Battiston;Guido Caldarelli;Guido Caldarelli;Massimo Riccaboni
(2020)Adrià Plazas;Irene Malvestio;Michele Starnini;Albert Díaz-Guilera
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French Institute for Research in Computer Science and Automation - INRIA
Publications: 1
