Example of Computational Social Networks format
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Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format
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Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format Example of Computational Social Networks format
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open access Open Access

Computational Social Networks — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Modeling and Simulation #113 of 290 down down by 93 ranks
Computer Science Applications #292 of 693 down down by 196 ranks
Information Systems #144 of 329 down down by 96 ranks
Human-Computer Interaction #64 of 120 down down by 42 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 44 Published Papers | 139 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/06/2020
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Related Journals

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recommended Recommended

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CiteRatio: 10.5
SJR: 0.781
SNIP: 2.019
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CiteRatio: 23.3
SJR: 3.109
SNIP: 3.707
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CiteRatio: 6.4
SJR: 0.786
SNIP: 2.027
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Taylor and Francis

Quality:  
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CiteRatio: 5.9
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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.

3.2

113% from 2019

CiteRatio for Computational Social Networks from 2016 - 2020
Year Value
2020 3.2
2019 1.5
2018 3.3
2017 5.0
graph view Graph view
table view Table view

0.325

60% from 2019

SJR for Computational Social Networks from 2018 - 2020
Year Value
2020 0.325
2019 0.203
2018 0.231
graph view Graph view
table view Table view

0.865

40% from 2019

SNIP for Computational Social Networks from 2017 - 2020
Year Value
2020 0.865
2019 0.619
2018 0.449
2017 1.152
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 113% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 60% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 40% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Computational Social Networks

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Springer

Computational Social Networks

Approved by publishing and review experts on SciSpace, this template is built as per for Computational Social Networks formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 587 authors to write and format their manuscripts to this journal.

Therory of social computing

i
Last updated on
08 Jun 2020
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ISSN
1606-8610
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/S40649-019-0069-Y
Graph convolutional networks: a comprehensive review
Si Zhang1, Hanghang Tong1, Jiejun Xu2, Ross Maciejewski3

Abstract:

Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challengin... Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research. read more read less

Topics:

Graph (abstract data type) (70%)70% related to the paper, Graph property (67%)67% related to the paper, Deep learning (60%)60% related to the paper, Feature learning (57%)57% related to the paper
View PDF
562 Citations
open accessOpen access Journal Article DOI: 10.1186/S40649-017-0042-6
Stance and influence of Twitter users regarding the Brexit referendum
Miha Grčar1, Darko Cherepnalkoski1, Igor Mozetič1, Petra Kralj Novak1

Abstract:

Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and ... Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contra-Brexit camps? First, we construct a stance classification model by machine learning methods, and are then able to predict the stance of about one million UK-based Twitter users. The demography of Twitter users is, however, very different from the demography of the voters. By applying a simple age-adjusted mapping to the overall Twitter stance, the results show the prevalence of the pro-Brexit voters, something unexpected by most of the opinion polls. Second, we apply the Hirsch index to estimate the influence, and rank the Twitter users from both camps. We find that the most productive Twitter users are not the most influential, that the pro-Brexit camp was four times more influential, and had considerably larger impact on the campaign than the opponents. Third, we find that the top pro-Brexit communities are considerably more polarized than the contra-Brexit camp. These results show that social media provide a rich resource of data to be exploited, but accumulated knowledge and lessons learned from the opinion polls have to be adapted to the new data sources. read more read less

Topics:

Social media (61%)61% related to the paper
View PDF
83 Citations
open accessOpen access Journal Article DOI: 10.1186/S40649-014-0003-2
A Robust Information Source Estimator with Sparse Observations
Kai Zhu1, Lei Ying1

Abstract:

Purpose/Background: In this paper, we consider the problem of locating the information source with sparse observations. We assume that a piece of information spreads in a network following a heterogeneous susceptible-infected-recovered (SIR) model, where a node is said to be infected when it receives the information and recov... Purpose/Background: In this paper, we consider the problem of locating the information source with sparse observations. We assume that a piece of information spreads in a network following a heterogeneous susceptible-infected-recovered (SIR) model, where a node is said to be infected when it receives the information and recovered when it removes or hides the information. We further assume that a small subset of infected nodes are reported, from which we need to find the source of the information. Methods: We adopt the sample path-based estimator developed in the work of Zhu and Ying (arXiv:1206.5421, 2012) and prove that on infinite trees, the sample path-based estimator is a Jordan infection center with respect to the set of observed infected nodes. In other words, the sample path-based estimator minimizes the maximum distance to observed infected nodes. We further prove that the distance between the estimator and the actual source is upper bounded by a constant independent of the number of infected nodes with a high probability on infinite trees. Results: Our simulations on tree networks and real-world networks show that the sample path-based estimator is closer to the actual source than several other algorithms. Conclusions: In this paper, we proposed the sample path-based estimator for information source localization. Both theoretic analysis and numerical evaluations showed that the sample path-based estimator is robust and close to the real source. read more read less

Topics:

Estimator (60%)60% related to the paper, Information source (mathematics) (53%)53% related to the paper, Minimax estimator (52%)52% related to the paper, Tree (graph theory) (50%)50% related to the paper
View PDF
72 Citations
open accessOpen access Book Chapter DOI: 10.1007/978-1-4471-4054-2_12
Extraction and Analysis of Facebook Friendship Relations

Abstract:

Online social networks (OSNs) are a unique web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of online social networks both from the point of view of marketing and offer of new services and from a scie... Online social networks (OSNs) are a unique web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of online social networks both from the point of view of marketing and offer of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (off-line) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem). However, OSN analysis poses novel challenges both to computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations is restricted; thus, we acquired the necessary information directly from the front end of the website, in order to reconstruct a subgraph representing anonymous interconnections among a significant subset of users. We describe our ad hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first-search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms. read more read less

Topics:

Social network (60%)60% related to the paper, Dynamic network analysis (60%)60% related to the paper, Social web (60%)60% related to the paper, Evolving networks (59%)59% related to the paper, Social computing (59%)59% related to the paper
View PDF
64 Citations
open accessOpen access Book Chapter DOI: 10.1007/978-1-4471-4051-1_4
Privacy in Online Social Networks
Michael Beye1, Arjan Jeckmans2, Zekeriya Erkin1, Pieter H. Hartel2, Reginald L. Lagendijk1, Qiang Tang2

Abstract:

Online social networks (OSNs) have become part of daily life for millions of users. Users building explicit networks that represent their social relationships and often share a wealth of personal information to their own benefit. The potential privacy risks of such behavior are often underestimated or ignored. The problem is ... Online social networks (OSNs) have become part of daily life for millions of users. Users building explicit networks that represent their social relationships and often share a wealth of personal information to their own benefit. The potential privacy risks of such behavior are often underestimated or ignored. The problem is exacerbated by lacking experience and awareness in users, as well as poorly designed tools for privacy management on the part of the OSN. Furthermore, the centralized nature of OSNs makes users dependent and puts the service provider in a position of power. Because service providers are not by definition trusted or trustworthy, their practices need to be taken into account when considering privacy risks. This chapter aims to provide insight into privacy in OSNs. First, a classification of different types of OSNs based on their nature and purpose is made. Next, different types of data contained in OSNs are distinguished. The associated privacy risks in relation to both users and service providers are identified, and finally, relevant research areas for privacy-protecting techniques are discussed. Clear mappings are made to reflect typical relations that exist between OSN type, data type, particular privacy risks, and privacy-preserving solutions. read more read less

Topics:

Information privacy (65%)65% related to the paper, Privacy by Design (64%)64% related to the paper, Privacy software (62%)62% related to the paper, Personally identifiable information (57%)57% related to the paper, Service provider (55%)55% related to the paper
View PDF
56 Citations
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Frequently asked questions

1. Can I write Computational Social Networks in LaTeX?

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

2. Do you follow the Computational Social Networks guidelines?

Yes, the template is compliant with the Computational Social Networks 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 Computational Social Networks?

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 Computational Social Networks citation style.

4. Can I use the Computational Social Networks 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 Computational Social Networks.

5. Can I use a manuscript in Computational Social Networks 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 Computational Social Networks that you can download at the end.

6. How long does it usually take you to format my papers in Computational Social Networks?

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

7. Where can I find the template for the Computational Social Networks?

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 Computational Social Networks'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 Computational Social Networks'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. Computational Social Networks an online tool or is there a desktop version?

SciSpace's Computational Social Networks 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 Computational Social Networks?

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 Computational Social Networks?”

11. What is the output that I would get after using Computational Social Networks?

After writing your paper autoformatting in Computational Social Networks, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Computational Social Networks'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 Computational Social Networks?

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 Computational Social Networks. 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 Computational Social Networks?

The 5 most common citation types in order of usage for Computational Social Networks 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 Computational Social Networks?

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16. Can I download Computational Social Networks 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 Computational Social Networks Endnote style according to Elsevier guidelines.

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