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Computational Social Networks Template

Write in a clean editor, then format for Computational Social Networks in one click — DocuGuru applies the official Springer Nature template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.

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About the Computational Social Networks format

Computational Social Networks is a peer-reviewed journal published by Springer Nature, covering Complex Network Analysis Techniques, Opinion Dynamics and Social Influence, Social Media and Politics.

PublisherSpringer Nature
Reference styleSuperscript numbered (Nature)
Superscript — small raised numerals in the text
1. Smith, A., Jones, B. & Lee, C. A representative article title. Computational Social Networks 12, 45–58 (2023).

Formats any DOI in Computational Social Networks style. No sign-up.

Publishes research inComplex Network Analysis Techniques Opinion Dynamics and Social Influence Social Media and Politics Opportunistic and Delay-Tolerant Networks Advanced Graph Neural Networks
ISSN2197-4314
h-index26
i10-index67
Total citations4,031
Top institutions publishing hereUniversity of Central Florida
Journal websitecomputationalsocialnetworks.springeropen.com
You getA submission-ready PDF and the editable LaTeX source — ready to submit.

Papers published in Computational Social Networks per year

1
1992
1
2005
45
2012
7
2014
18
2015
11
2016
12
2017
12
2018
14
2019
6
2020
22
2021

Citation impact of Computational Social Networks by publication year

6
1992
0
2005
414
2012
207
2014
315
2015
151
2016
207
2017
156
2018
2.1K
2019
165
2020
294
2021

Citations each year’s papers have accumulated so far — the most recent years are still building up.

Most-cited papers in Computational Social Networks

Graph convolutional networks: a comprehensive review

Si Zhang, Hanghang Tong, Jiejun Xu et al. · 10 Nov 2019

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)…

1,830 citations Cite SaveGo to paper →
A review: preprocessing techniques and data augmentation for sentiment analysis

H. T. Duong, Tram-Anh Nguyen-Thi · 6 Jan 2021

Abstract In literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedious, expensive and time-consuming to build, and hard to handle unseen data. This paper has approached semi-supervised learning for Vietnamese sentiment analysis which has limited…

144 citations Cite SaveGo to paper →
Stance and influence of Twitter users regarding the Brexit referendum

Miha Grćar, Darko Cherepnalkoski, Igor Mozetič et al. · 17 Jul 2017

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…

101 citations Cite SaveGo to paper →
A robust information source estimator with sparse observations

K. J. Zhu, Lei Ying · 14 Oct 2014

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.…

83 citations Cite SaveGo to paper →
Efficiently identifying critical nodes in large complex networks

Mario Ventresca, Dionne M. Aleman · 27 Mar 2015

The critical node detection problem (CNDP) aims to fragment a graph G=(V,E) by removing a set of vertices R with cardinality |R|≤k, such that the residual graph has minimum pairwise connectivity for user-defined value k. Existing optimization algorithms are incapable of finding a good set R in graphs with many thousands or millions of vertices…

64 citations Cite SaveGo to paper →

Computational Social Networks template — frequently asked questions

How do I write a paper in the Computational Social Networks format?
In DocuGuru you write your manuscript in a normal editor — no LaTeX setup required — and select the Computational Social Networks template. When you export, DocuGuru compiles the paper into the official Springer Nature format and hands you a submission-ready PDF along with the editable LaTeX source.
What reference style does Computational Social Networks use?
Computational Social Networks uses Superscript numbered (Nature) references, shown as superscript numerals in the text. DocuGuru formats every in-text citation and the reference list in this exact style automatically. A reference appears like this: 1. Smith, A., Jones, B. & Lee, C. A representative article title. Computational Social Networks 12, 45–58 (2023).
Do I need to know LaTeX to submit to Computational Social Networks?
No. DocuGuru generates the sn-jnl LaTeX class and compiles the PDF for you in the background, so you get a Springer Nature-ready Computational Social Networks document without writing any LaTeX. If you do want it, the LaTeX source is included in the export.
Can I import an existing draft into the Computational Social Networks template?
Yes. Paste or upload your current manuscript — Word, LaTeX, Markdown, or plain text — and DocuGuru reflows it into the Computational Social Networks format with correct headings, figures, tables, and superscript citations.
Who publishes Computational Social Networks?
Computational Social Networks is a multidisciplinary journal published by Springer Nature. DocuGuru's Computational Social Networks template matches Springer Nature's official submission format.
Can I export a submission-ready Computational Social Networks PDF?
Yes — DocuGuru produces a PDF built with the official Computational Social Networks template (the sn-jnl class) that is ready to submit to Springer Nature, together with the matching LaTeX source files.
How much does the Computational Social Networks template cost?
You can start writing in the Computational Social Networks template for free. Exporting the final submission-ready Computational Social Networks PDF and LaTeX source is part of DocuGuru's paid plans — see the app for current pricing.
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