Example of Applied Network Science format
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Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format
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Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format Example of Applied Network Science format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Applied Network Science — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Multidisciplinary #25 of 110 down down by None rank
Computational Mathematics #75 of 152 down down by None rank
Computer Networks and Communications #178 of 334 down down by None rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 309 Published Papers | 707 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 21/07/2020
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Related Journals

open access Open Access

Inderscience Publishers

Quality:  
Good
CiteRatio: 1.9
SJR: 0.246
SNIP: 1.938
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recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 5.0
SJR: 0.632
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open access Open Access

Taylor and Francis

Quality:  
High
CiteRatio: 1.4
SJR: 0.214
SNIP: 0.992
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 8.6
SJR: 1.031
SNIP: 3.478

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

2.3

53% from 2019

CiteRatio for Applied Network Science from 2016 - 2020
Year Value
2020 2.3
2019 1.5
graph view Graph view
table view Table view

0.407

13% from 2019

SJR for Applied Network Science from 2019 - 2020
Year Value
2020 0.407
2019 0.466
graph view Graph view
table view Table view

0.889

19% from 2019

SNIP for Applied Network Science from 2019 - 2020
Year Value
2020 0.889
2019 0.748
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 53% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 13% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 19% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Applied Network Science

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Springer

Applied Network Science

Approved by publishing and review experts on SciSpace, this template is built as per for Applied Network Science formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 396 authors to write and format their manuscripts to this journal.

Real world problems

i
Last updated on
21 Jul 2020
i
ISSN
2364-8228
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Citation Type
Numbered
(Blonder et al, 1982)
i
Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM (1982) Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B 25(7):4515_x0015_ 4532, URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1007/S41109-019-0189-1
Urban Spatial Order: Street Network Orientation, Configuration, and Entropy
Geoff Boeing1
23 Aug 2019 - Applied Network Science

Abstract:

Street networks may be planned according to clear organizing principles or they may evolve organically through accretion, but their configurations and orientations help define a city’s spatial logic and order. Measures of entropy reveal a city’s streets’ order and disorder. Past studies have explored individual cases of orien... Street networks may be planned according to clear organizing principles or they may evolve organically through accretion, but their configurations and orientations help define a city’s spatial logic and order. Measures of entropy reveal a city’s streets’ order and disorder. Past studies have explored individual cases of orientation and entropy, but little is known about broader patterns and trends worldwide. This study examines street network orientation, configuration, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx. It measures the entropy of street bearings in weighted and unweighted network models, along with each city’s typical street segment length, average circuity, average node degree, and the network’s proportions of four-way intersections and dead-ends. It also develops a new indicator of orientation-order that quantifies how a city’s street network follows the geometric ordering logic of a single grid. A cluster analysis is performed to explore similarities and differences among these study sites in multiple dimensions. Significant statistical relationships exist between city orientation-order and other indicators of spatial order, including street circuity and measures of connectedness. On average, US/Canadian study sites are far more grid-like than those elsewhere, exhibiting less entropy and circuity. These indicators, taken in concert, help reveal the extent and nuance of the grid. These methods demonstrate automatic, scalable, reproducible tools to empirically measure and visualize city spatial order, illustrating complex urban transportation system patterns and configurations around the world. read more read less

Topics:

Street network (64%)64% related to the paper
View PDF
139 Citations
open accessOpen access Journal Article DOI: 10.1007/S41109-017-0023-6
The many facets of community detection in complex networks.
15 Feb 2017 - Applied Network Science

Abstract:

Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular for... Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research. read more read less

Topics:

Network science (52%)52% related to the paper, Complex network (51%)51% related to the paper
View PDF
131 Citations
open accessOpen access Journal Article DOI: 10.1007/S41109-019-0195-3
A survey on graph kernels
Nils M. Kriege1, Fredrik D. Johansson2, Christopher Morris1
01 Dec 2020 - Applied Network Science

Abstract:

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, su... Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification. read more read less

Topics:

Graph kernel (66%)66% related to the paper, Radial basis function kernel (58%)58% related to the paper
View PDF
125 Citations
open accessOpen access Journal Article DOI: 10.1007/S41109-018-0067-2
Connectivity and complex systems: learning from a multi-disciplinary perspective
18 Jun 2018 - Applied Network Science

Abstract:

In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this... In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. However, any implementation of the concept of connectivity carries with it both ontological and epistemological constraints, which leads us to ask if there is one type or set of approach(es) to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges in using connectivity to understand complex systems from the standpoint of widely different disciplines. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon discipline-specific insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. We evaluate how a connectivity-based approach has generated new understanding of structural-functional relationships that characterise complex systems and propose a ‘common toolbox’ underpinned by network-based approaches that can advance connectivity studies by overcoming existing constraints. read more read less
View PDF
108 Citations
open accessOpen access Journal Article DOI: 10.1007/S41109-019-0238-9
On community structure in complex networks: challenges and opportunities
Hocine Cherifi1, Gergely Palla2, Boleslaw K. Szymanski3, Xiaoyan Lu3
14 Aug 2019 - Applied Network Science

Abstract:

Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigation... Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed in order to better understand the impact of community structure and its dynamics on networked systems. Here, we first focus on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. We discuss modularity and the use of modularity maximization as the basis for community detection. Then, we follow with an overview of the Stochastic Block Model and its different variants as well as inference of community structures from such models. Next, we focus on time evolving networks, where existing nodes and links can disappear, and in parallel new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. We briefly discuss considerable advances made in this field recently. Finally, we focus on immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network. read more read less

Topics:

Evolving networks (58%)58% related to the paper, Modularity (networks) (58%)58% related to the paper, Complex network (55%)55% related to the paper, Stochastic block model (52%)52% related to the paper
View PDF
107 Citations
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Frequently asked questions

1. Can I write Applied Network Science in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Applied Network Science guidelines and auto format it.

2. Do you follow the Applied Network Science guidelines?

Yes, the template is compliant with the Applied Network Science guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Applied Network Science?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Applied Network Science citation style.

4. Can I use the Applied Network Science templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Applied Network Science.

5. Can I use a manuscript in Applied Network Science that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Applied Network Science that you can download at the end.

6. How long does it usually take you to format my papers in Applied Network Science?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Applied Network Science.

7. Where can I find the template for the Applied Network Science?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Applied Network Science's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Applied Network Science's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Applied Network Science an online tool or is there a desktop version?

SciSpace's Applied Network Science is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Applied Network Science?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Applied Network Science?”

11. What is the output that I would get after using Applied Network Science?

After writing your paper autoformatting in Applied Network Science, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Applied Network Science's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Applied Network Science?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Applied Network Science. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Applied Network Science?

The 5 most common citation types in order of usage for Applied Network Science are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Applied Network Science?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Applied Network Science's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Applied Network Science in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Applied Network Science Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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