Recent Advances in Social Networks and Social Media (2nd Edition)

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 August 2026 | Viewed by 433

Special Issue Editor


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Guest Editor
Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
Interests: combinatorics of networks; algorithmic graph theory; parallel and distributed algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Studies of social networks have been conducted for a century in various disciplines such as sociology, psychology, economics, and anthropology. Recent advances in the Internet, social webs, and other large-scale social and technological infrastructure have triggered a growing interest and significant methodological advancements in social network analysis and mining. Inspired by these research problems, new methods from graph theory, machine learning and data mining, statistics, and statistical mechanics have been developed, which in turn have opened up further possibilities for more interesting applications, leading to a rising prominence of analysis of social networks and social media using different methods and tools from academia, business, and politics.

We are excited to announce a Special Issue of the journal Computers titled "Recent Advances in Social Networks and Social Media (2nd Edition)". In this Special Issue, we aim to collate the latest research on recent developments in social networks and social media by welcoming the submission of novel research articles, comprehensive reviews, and survey articles. Extended conference papers are also welcome. Papers should contain at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

Topics of interest include, but are not limited to, the following:

  • network formation;
  • social networks;
  • network experiments;
  • local interaction games;
  • non-cooperative games;
  • real-world complex network analysis;
  • pattern analysis in social networks;
  • temporal networks;
  • information diffusion models;
  • reputation and trust in social media;
  • social influence, recommendation, and media.

Prof. Dr. Hovhannes Harutyunyan
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • network formation
  • social networks
  • network experiments
  • local interaction games
  • non-cooperative games
  • real-world complex network analysis
  • pattern analysis in social networks
  • temporal networks
  • information diffusion models
  • reputation and trust in social media
  • social influence, recommendation, and media

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Published Papers (1 paper)

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Research

19 pages, 3121 KB  
Article
TrustGTN: A Social Network Trust Evaluation Method Based on Heterogeneous Graph Neural Network
by Xiao Liu, Zai Yang, Jining Chen and Gaoxiang Li
Computers 2026, 15(3), 176; https://doi.org/10.3390/computers15030176 - 9 Mar 2026
Viewed by 237
Abstract
The rapid growth of social networks and online platforms has heightened the importance of trust evaluation in various applications, including e-commerce, social networking, online collaboration, and mobile crowdsourcing. Traditional trust evaluation methods often rely on handcrafted features and simple models, which fail to [...] Read more.
The rapid growth of social networks and online platforms has heightened the importance of trust evaluation in various applications, including e-commerce, social networking, online collaboration, and mobile crowdsourcing. Traditional trust evaluation methods often rely on handcrafted features and simple models, which fail to fully capture the implicit patterns within the complex, heterogeneous structures of social networks. To address this issue, we propose TrustGTN, a novel method based on Heterogeneous Graph Neural Networks (HGNNs). It incorporates a soft selection mechanism that dynamically adjusts the training matrix weights. This enables it to capture the evolving structural and semantic patterns of the graph. The model can automatically learn important trust chains without the need to manually set their lengths. Experimental results show that TrustGTN outperforms existing trust evaluation methods on public datasets, demonstrating its advantages in handling heterogeneous graph data. Full article
(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media (2nd Edition))
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