Special Issue "Recent Advances in Social Data and Artificial Intelligence 2019"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Guest Editor
Prof. Dr. H. M. Srivastava grade Website E-Mail
Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3R4, Canada
Interests: Real and Complex Analysis; Fractional Calculus and Its Applications; Integral Equations and Transforms; Higher Transcendental Functions and Their Applications; q-Series and q-Polynomials; Analytic Number Theory; Analytic and Geometric Inequalities Probability and Statistics; Inventory Modelling and Optimization.
Guest Editor
Prof. Gautam Srivastava Website E-Mail
Brandon University, Canada
Interests: Blockchain Technology; Cryptography; Big Data; Data Mining; Social Networks; Security and Privacy; Anonymity and Graphs.
Guest Editor
Prof. Vijay Mago Website E-Mail
Lakehead University, Canada
Interests: Social data analysis; Artificial Intelligence; Big data; Health Informatics; Medical decision making

Special Issue Information

Dear Colleagues,

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This Special Issue has emerged from the International Conference on Social Data and Artificial Intelligence (SDAI 2019) (Minneapolis, Minnesota, U.S.A.; August 7-9, 2019).

In this Special Issue, we cordially invite and welcome review, expository, and original research articles dealing with the recent advances in the subjects of social data and artificial intelligence, not only from the participants and speakers associated with SDAI 2019, but also from other authors researching these subjects of current interest.

Prof. H. M. Srivastava
Prof. Gautam Srivastava
Prof. Vijay Mago
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Symmetry 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 1400 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.


  • Social data inadequacies and inconsistencies
  • Predictive models of social behaviors
  • Infrastructure and architecture for testing social theories
  • Data collection and analysis platforms
  • Relevance of IoT for social science theories
  • Building capacity to continuously collect data across a range of social media networks
  • Designing efficient parsers to deal with noisy social media data-sets for real-time tracking of health issues, diseases, and wellness
  • Designing tools to map and measure the effectiveness of health campaigns by healthcare organizations
  • Cross-validating the predictive models of social media data-sets with ground truth data
  • Developing frameworks and algorithms to perform real-time analysis of social media data-sets

Published Papers (1 paper)

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Open AccessArticle
Centrality Metrics’ Performance Comparisons on Stock Market Datasets
Symmetry 2019, 11(7), 916; https://doi.org/10.3390/sym11070916 - 15 Jul 2019
The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data [...] Read more.
The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Symmetry, EISSN 2073-8994, Published by MDPI AG
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