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Social Network Analysis and Mining

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 7516

Special Issue Editors


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Guest Editor
School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: cloud computing; multimedia content processing; semantic web; social networking technologies

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Guest Editor
Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Interests: edge/cloud computing; distributed systems; IoT and applications; blockchains
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social network analysis (SNA) and social network mining have gained significant momentum during the last decade as they have been extensively applied within social media and networks as part of commercial services. Today, social networks have a large base of daily users. Especially after the COVID-19 pandemic, we have witnessed that people tend to use social networks to enhance their social life and increase interaction with the community, while it became apparent that widespread capabilities of disinformation propagation through social media is something that attracts the interest of policy makers as a problem to be counter-measured.

Social network analysis is harnessing knowledge within organisations, which reveals relationships between people within communities and organizations, and can form the basis of utilizing the collective intelligence in communities and organizations.

Given this growing importance and interest in SNA, we have decided to dedicate a Special Issue on this very interesting and multidisciplinary research topic.

Topics of Interest: We invite contributions on theory and practice, including but not limited to the following areas:

  • Disinformation through social networks and media
  • Trusted and reliable social networks
  • Open Data and Social Network analysis
  • SNA: Modeling, prediction, simulation, and evaluation
  • Social network mining and data storage
  • Social networks for health care
  • Information and knowledge management
  • Artificial intelligence and social network analysis
  • Distributed ledger technologies, blockchains, and social networks

Prof. Dr. Theodora Varvarigou
Dr. Antonios Litke
Guest Editors

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • social network analysis
  • social network mining
  • disinformation detection and propagation analysis
  • SNA modelling
  • SNA in healthcare
  • AI and social networks
  • DLTs and decentralized social networks

Published Papers (4 papers)

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Research

17 pages, 4127 KiB  
Article
Leveraging Machine Learning and Semi-Structured Information to Identify Political Views from Social Media Posts
by Adriana Olteanu, Alexandra Cernian and Sebastian-Augustin Gâgă
Appl. Sci. 2022, 12(24), 12962; https://doi.org/10.3390/app122412962 - 16 Dec 2022
Cited by 1 | Viewed by 1418
Abstract
Social media platforms make a significant contribution to modeling and influencing people’s opinions and decisions, including political views and orientation. Analyzing social media content can reveal trends and key triggers that will influence society. This paper presents an exhaustive analysis of the performance [...] Read more.
Social media platforms make a significant contribution to modeling and influencing people’s opinions and decisions, including political views and orientation. Analyzing social media content can reveal trends and key triggers that will influence society. This paper presents an exhaustive analysis of the performance generated by various implementations of the Naïve Bayes classifier, combined with a semi-structured information approach, to identify the political orientation of Twitter users, based on their posts. As research methodology, we aggregate in a semi-structured format a database of over 86,000 political posts from Democrat (right) and Republican (left) ideologies. Such an approach allows us to associate a Democrat or Republican label to each tweet, in order to create and train the model. The semi-structured input data are processed using several NLP techniques and then the model is trained to classify the political orientation based on semantic criteria and semi-structured information. This paper examines several variations of the Naïve Bayes classifier suite: Gaussian Naïve Bayes, Multinomial Naïve Bayes, Calibrated Naïve Bayes algorithms, and tracks a variety of performance indices and their graphical representations: Prediction Accuracy, Precision, Recall, Confusion Matrix, Brier Score Loss, etc. We obtained an accuracy of around 80–85% in identifying the political orientation of the users. This leads us to the conclusion that this type of application can be integrated into a more complex system and can help in determining political trends or election results. Full article
(This article belongs to the Special Issue Social Network Analysis and Mining)
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15 pages, 555 KiB  
Article
Detecting Anomalies in Network Communities Based on Structural and Attribute Deviation
by Hedia Zardi, Hanen Karamti, Walid Karamti and Norah Saleh Alghamdi
Appl. Sci. 2022, 12(22), 11791; https://doi.org/10.3390/app122211791 - 20 Nov 2022
Cited by 1 | Viewed by 1164
Abstract
Anomaly detection in online social networks (OSNs) is an important data mining task that aims to detect unexpected and suspicious users. To enhance anomaly exploration, anomaly ranking is used to assess the degree of user anomaly rather than applying binary detection methods, which [...] Read more.
Anomaly detection in online social networks (OSNs) is an important data mining task that aims to detect unexpected and suspicious users. To enhance anomaly exploration, anomaly ranking is used to assess the degree of user anomaly rather than applying binary detection methods, which depend on identifying users as either anomalous users or normal users. In this paper, we propose a community-based anomaly detection approach called Community ANOMaly detection (CAnom). Our approach aims to detect anomalous users in an OSN community and rank them based on their degree of deviation from normal users. Our approach measures the level of deviation in both the network structure and a subset of the attributes, which is defined by the context selection. The approach consists of two phases. First, we partition the network into communities. Then, we compute the anomaly ranking score, which is composed of a community-structure-based score and an attribute-based score. Experiments on real-world benchmark datasets show that CAnom detects ground-truth groups and outperforms baseline algorithms on accuracy. On synthetic datasets, the results show that CAnom has high AUC and ROC curves even when the attribute number increases; therefore, our model is suitable for today’s applications, where the number of attributes is rising. Full article
(This article belongs to the Special Issue Social Network Analysis and Mining)
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17 pages, 6656 KiB  
Article
Land Use Identification through Social Network Interaction
by Jesus S. Aguilar-Ruiz, Diana C. Pauca-Quispe, Cinthya Butron-Revilla, Ernesto Suarez-Lopez and Karla Aranibar-Tila
Appl. Sci. 2022, 12(17), 8580; https://doi.org/10.3390/app12178580 - 27 Aug 2022
Cited by 2 | Viewed by 1691
Abstract
The Internet generates large volumes of data at a high rate, in particular, posts on social networks. Although social network data have numerous semantic adulterations and are not intended to be a source of geo-spatial information, in the text of posts we find [...] Read more.
The Internet generates large volumes of data at a high rate, in particular, posts on social networks. Although social network data have numerous semantic adulterations and are not intended to be a source of geo-spatial information, in the text of posts we find pieces of important information about how people relate to their environment, which can be used to identify interesting aspects of how human beings interact with portions of land based on their activities. This research proposes a methodology for the identification of land uses using Natural Language Processing (NLP) from the contents of the popular social network Twitter. It will be approached by identifying keywords with linguistic patterns from the text, and the geographical coordinates associated with the publication. Context-specific innovations are introduced to deal with data across South America and, in particular, in the city of Arequipa, Peru. The objective is to identify the five main land uses: residential, commercial, institutional-governmental, industrial-offices and unbuilt land. Within the framework of urban planning and sustainable urban management, the methodology contributes to the optimization of the identification techniques applied for the updating of land use cadastres, since the results achieved an accuracy of about 90%, which motivates its application in the real context. In addition, it would allow the identification of land use categories at a more detailed level, in situations such as a complex/mixed distribution building based on the amount of data collected. Finally, the methodology makes land use information available in a more up-to-date fashion and, above all, avoids the high economic cost of the non-automatic production of land use maps for cities, mostly in developing countries. Full article
(This article belongs to the Special Issue Social Network Analysis and Mining)
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13 pages, 721 KiB  
Article
Link Pruning for Community Detection in Social Networks
by Jeongseon Kim, Soohwan Jeong and Sungsu Lim
Appl. Sci. 2022, 12(13), 6811; https://doi.org/10.3390/app12136811 - 5 Jul 2022
Viewed by 1651
Abstract
Attempts to discover knowledge through data are gradually becoming diversified to understand complex aspects of social phenomena. Graph data analysis, which models and analyzes complex data as graphs, draws much attention as it combines the latest machine learning techniques. In this paper, we [...] Read more.
Attempts to discover knowledge through data are gradually becoming diversified to understand complex aspects of social phenomena. Graph data analysis, which models and analyzes complex data as graphs, draws much attention as it combines the latest machine learning techniques. In this paper, we propose a new framework called link pruning for detecting clusters in complex networks, which leverages the cohesiveness of local structures by removing unimportant connections. Link pruning is a flexible framework that reduces the clustering problem in a highly mixed community structure to a simpler problem with a lowly mixed community structure. We analyze which similarities and curvatures defined on the pairs of nodes, which we call the link attributes, allow links inside and outside the community to have a different range of values. Using the link attributes, we design and analyze an algorithm that eliminates links with low attribute values to find a better community structure on the transformed graph with low mixing. Through extensive experiments, we have shown that clustering algorithms with link pruning achieve higher quality than existing algorithms in both synthetic and real-world social networks. Full article
(This article belongs to the Special Issue Social Network Analysis and Mining)
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