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Electronics
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Data-Driven AI Approaches with Applications in Social Network, Media Analytics...
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Data-Driven AI Approaches with Applications in Social Network, Media Analytics and Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 1426

Special Issue Editors

Prof. Dr. Lichuan Liu

E-Mail Website
Guest Editor
Department of Electrical Engineering, Northern Illinois University, Dekalb, IL 60115, USA
Interests: AI; digital signal processing
Dr. Wei Li

E-Mail Website
Guest Editor
Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada
Interests: applied AI in automation, agriculture, health and finance; cloud computing; Internet of Things; cyber security and forensics; standardization of AI technologies; information theory

Special Issue Information

Dear Colleagues,

The ever-increasing complexity of networked systems in today's interconnected universe presents a significant challenge in understanding their structure and dynamics. Addressing this challenge requires advanced computational predictive analytics and data-driven methodologies to characterize and predict phenomena across various spatiotemporal scales.

This Special Issue seeks to showcase recent advancements, applications, and contributions in the fields of Artificial Intelligence (AI), machine learning methods, data analysis, big data analytics, and computational complexity. We encourage submissions that explore topics such as advanced data analysis and visualization in complex models, big data analysis using multifractal and fractional calculus methods, and AI approaches for real-world scenarios.

We welcome contributions on a wide range of topics including fractional calculus and complex systems, optimization algorithms for complex systems, machine learning applications in complex data, AI applications in signal processing, and advanced computational imaging.

Researchers and practitioners are invited to submit original research articles, reviews, or case studies to this Special Issue. All submissions will undergo a rigorous peer-review process to ensure high scientific quality and will be published online in an open-access format.

We look forward to receiving your valuable contributions to this Special Issue, which promises to advance our understanding of data-driven AI approaches and their applications in complex systems.

Prof. Dr. Lichuan Liu
Prof. Wei Li
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.

Keywords

  • artificial intelligence (AI)
  • machine learning
  • data analysis
  • big-data analytics
  • data-driven application

Published Papers (2 papers)

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Research

24 pages, 670 KiB  
Article
Influence Maximization Based on Adaptive Graph Convolution Neural Network in Social Networks
by Wei Liu, Saiwei Wang and Jiayi Ding
Electronics 2024, 13(16), 3110; https://doi.org/10.3390/electronics13163110 - 6 Aug 2024
Viewed by 276
Abstract
The influence maximization problem is a hot issue in the research on social networks due to its wide application. The problem aims to find a small subset of influential nodes to maximize the influence spread. To tackle the challenge of striking a balance [...] Read more.
The influence maximization problem is a hot issue in the research on social networks due to its wide application. The problem aims to find a small subset of influential nodes to maximize the influence spread. To tackle the challenge of striking a balance between efficiency and effectiveness in traditional influence maximization algorithms, deep learning-based influence maximization algorithms have been introduced and have achieved advancement. However, these algorithms still encounter two key problems: (1) Traditional deep learning models are not well-equipped to capture the latent topological information of networks with varying sizes and structures. (2) Many deep learning-based methods use the influence spread of individual nodes as labels to train a model, which can result in an overlap of influence among the seed nodes selected by the model. In this paper, we reframe the influence maximization problem as a regression task and introduce an innovative approach to influence maximization. The method adopts an adaptive graph convolution neural network which can explore the latent topology information of the network and can greatly improve the performance of the algorithm. In our approach, firstly, we integrate several network-level attributes and some centrality metrics into a vector as the presentation vector of nodes in the social network. Next, we propose a new label generation method to measure the influence of nodes by neighborhood discount strategy, which takes full account of the influence overlapping problem. Subsequently, labels and presentation vectors are fed into an adaptive graph convolution neural network model. Finally, we use the well-trained model to predict the importance of nodes and select top-K nodes as a seed set. Abundant experiments conducted on various real-world datasets have confirmed that the performance of our proposed algorithm surpasses that of several current state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Data-Driven AI Approaches with Applications in Social Network, Media Analytics and Smart Cities)
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13 pages, 1596 KiB  
Article
Are Your Comments Positive? A Self-Distillation Contrastive Learning Method for Analyzing Online Public Opinion
by Dongyang Zhou, Lida Shi, Bo Wang, Hao Xu and Wei Huang
Electronics 2024, 13(13), 2509; https://doi.org/10.3390/electronics13132509 - 26 Jun 2024
Viewed by 778
Abstract
With the popularity of social media, online opinion analysis is becoming more and more widely and deeply used in management studies. Automatically recognizing the sentiment of user reviews is a crucial tool for opinion analysis research. However, previous studies mainly have focused on [...] Read more.
With the popularity of social media, online opinion analysis is becoming more and more widely and deeply used in management studies. Automatically recognizing the sentiment of user reviews is a crucial tool for opinion analysis research. However, previous studies mainly have focused on specific scenarios or algorithms that cannot be directly applied to real-world opinion analysis. To address this issue, we collect a new dataset of user reviews from multiple real-world scenarios such as e-retail, e-commerce, movie reviews, and social media. Due to the heterogeneity and complexity of this multi-scenario review data, we propose a self-distillation contrastive learning method. Specifically, we utilize two EMA (exponential moving average) models to generate soft labels as additional supervision. Additionally, we introduce the prototypical supervised contrastive learning module to reduce the variability of data in different scenarios by pulling in representations of the same class. Our method has proven to be extremely competitive, outperforming other advanced methods. Specifically, our method achieves an 87.44% F1 score, exceeding the performance of current advanced methods by 1.07%. Experimental results, including examples and visualization analysis, further demonstrate the superiority of our method. Full article
(This article belongs to the Special Issue Data-Driven AI Approaches with Applications in Social Network, Media Analytics and Smart Cities)
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