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Electronics
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Computational Intelligence in Social Big Data Analytics
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Computational Intelligence in Social Big Data Analytics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 4046

Special Issue Editors

Dr. Guanfeng Liu

E-Mail Website
Guest Editor
Department of Computing, Macquarie University, Sydney 2109, Australia
Interests: graph data mining; social networks; trust computing
Special Issues, Collections and Topics in MDPI journals
Dr. Jilin Hu

E-Mail Website
Guest Editor
Associate Professor, Department of Computer Science, Aalborg University, Selma Lagerlöfs Vej 300, 9220 Aalborg East, Denmark
Interests: spatio-temporal data management; traffic data analysis; graph neural networks
Prof. Dr. An Liu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Soochow University, Suzhou 215031, China
Interests: crowdsourcing; data management; recommender system

Special Issue Information

Dear Colleagues,

In the big data era, large volumes of social media data are generated in a short period of time, which we refer to as social big data. Social big data has significance in many areas, such as biology, transportation, medical science, physics, etc. Social big data analytics has been widely used in applications such as event detection, expert finding in social graphs, and social-media-based recommender systems. In these applications, due to the large scale of social big graph data, the complex attributes included in big social graphs, and the dynamic changes of big social graph structures, the requirements for computational intelligence in social big data analytics are becoming increasingly demanding. Some of these requirements include effective graph embedding techniques for high-dimension social big graph data; the prediction of social graph structures in dynamic social big data; deep-learning-based trust, security, and privacy management in social big data; as well as intelligent distributed and parallel algorithms for social big data search.

Therefore, it is necessary to develop mechanisms adopting computational intelligence in social big data analytics, helping to build a secure, effective, and efficient environment for big data analytics, further supporting applications based on social big data as a backbone.

This Special Issue on computational intelligence of social big data analytics is intended to bring together some important examples of the successful application of computational intelligence in social big data analytics. The Issue will serve as a reference for all researchers working in the area. This Special Issue solicits high-quality, original research contributions on the application of computational intelligence in social big graph data analytics, and aims to capture the state-of-the-art and stimulate further developments in related areas.

Topic

(1)  Computational intelligence in the trust, security, and privacy of social big data.

(2)  Computational intelligence in the storage of social big data.

(3)  Computational intelligence in the search of social big data.

(4)  Computational intelligence in distributed/parallel and real-time processing of social big data.

(5)  Computational intelligence in indexing social big data.

(6)  Computational intelligence in mining social big data.

(7)  Computational intelligence in social big data management platforms/systems.

(8)  The evaluation and benchmarking of using Computational Intelligence for social big data management.

(9)  Computational intelligence in social big data visualization.

(10) Computational intelligence in social big data-based applications.

Dr. Guanfeng Liu
Dr. Jilin Hu
Prof. Dr. An Liu
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 submissions that pass pre-check are 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. Electronics 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.

Published Papers (2 papers)

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Research

18 pages, 1929 KiB  
Article
Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot Capture
by Delong Han, Tao Meng and Min Li
Electronics 2023, 12(13), 2875; https://doi.org/10.3390/electronics12132875 - 29 Jun 2023
Viewed by 980
Abstract
Knowing how to effectively predict the scale of future information cascades based on the historical trajectory of information dissemination has become an important topic. It is significant for public opinion guidance; advertising; and hotspot recommendation. Deep learning technology has become a research hotspot [...] Read more.
Knowing how to effectively predict the scale of future information cascades based on the historical trajectory of information dissemination has become an important topic. It is significant for public opinion guidance; advertising; and hotspot recommendation. Deep learning technology has become a research hotspot in popularity prediction, but for complex social platform data, existing methods are challenging to utilize cascade information effectively. This paper proposes a novel end-to-end deep learning network CAC-G with cascade attention convolution (CAC). This model can stress the global information when learning node information and reducing errors caused by information loss. Moreover, a novel Dynamic routing-AT aggregation method is investigated and applied to aggregate node information to generate a representation of cascade snapshots. Then, the gated recurrent unit (GRU) is employed to learn temporal information. This study’s validity and generalization ability are verified in the experiments by applying CAC-G on two public datasets where CAC-G is better than the existing baseline methods. Full article
(This article belongs to the Special Issue Computational Intelligence in Social Big Data Analytics)
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23 pages, 2942 KiB  
Article
Leading Logistics Firms’ Re-Engineering through the Optimization of the Customer’s Social Media and Website Activity
by Damianos P. Sakas, Dimitrios P. Reklitis and Marina C. Terzi
Electronics 2023, 12(11), 2443; https://doi.org/10.3390/electronics12112443 - 28 May 2023
Cited by 5 | Viewed by 2482
Abstract
To acquire competitive differentiation nowadays, logistics businesses must adopt novel strategies. Logistics companies have to consider whether redesigning their marketing plan based on client social media activity and website activity might increase the effectiveness of their digital marketing strategy. Insights from this study [...] Read more.
To acquire competitive differentiation nowadays, logistics businesses must adopt novel strategies. Logistics companies have to consider whether redesigning their marketing plan based on client social media activity and website activity might increase the effectiveness of their digital marketing strategy. Insights from this study will be used to help logistics firms improve the effectiveness of their digital marketing as part of a marketing re-engineering and change management process. An innovative methodology was implemented. Collecting behavioral big data from the logistics companies’ social media and websites was the first step. Next, regression and correlation analyses were conducted, together with the creation of a fuzzy cognitive map simulation in order to produce optimization scenarios. The results revealed that re-engineering marketing strategies and customer behavioral big data can successfully affect important digital marketing performance metrics. Additionally, social media big data can affect change management and re-engineering processes by reducing operational costs and investing more in social media visibility and less in social media interactivity. The following figure presents the graphical presentation of the abstract. Full article
(This article belongs to the Special Issue Computational Intelligence in Social Big Data Analytics)
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