Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.1
3.7
Journals
BDCC
Special Issues
Research Progress in Artificial Intelligence and Social Network Analysis
share announcement

Research Progress in Artificial Intelligence and Social Network Analysis

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 4171

Special Issue Editors

Prof. Dr. Yong Tang

E-Mail Website
Guest Editor
School of Computer Science, South China Normal University, Guangzhou 510631, China
Interests: data intelligence; social network and humanistic computing
Prof. Dr. Chaobo He

E-Mail Website
Guest Editor
School of Computer Science, South China Normal University, Guangzhou 510631, China
Interests: graph data mining; graph data intelligence; educational computing; artificial intelligence education
Dr. Chengzhou Fu

E-Mail Website
Guest Editor
School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
Interests: social network analysis; community discovery; information security; medical big data

Special Issue Information

Dear Colleagues,

Relying on the extensive application of the Internet, social networks not only have a large number of users, but also overcome the constraints of geographical location, time and social role to narrow the distance between users, making communication costs lower and timeliness stronger, making them the preferred platform for people to make friends, interact and obtain information. The emerging social network paradigm provides enormous novel approaches for efficient and advanced networking communications, data analysis and platform construction, etc. 

With the development of artificial intelligence technology, combined with emerging artificial intelligence technology, the ability of social network data mining can be improved. Therefore, a series of new theories, methods and technologies have emerged. This Special Issue will bring together researchers and developers in academic, practical and industrial fields to focus on artificial intelligence technology and social network analysis.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Artificial intelligence technology and social network analysis;
  • Group intelligence collaboration, group intelligence perception and collaborative evolutionary computing;
  • Human-like intelligent collaboration and emotional computing;
  • Collaborative knowledge engineering, knowledge atlas and graph knowledge management;
  • Social network and group intelligence behavior analysis;
  • Social and online collaborative teaching systems and applications;
  • Collaborative application of education big data analysis and education resources;
  • Teaching evaluation and learning behavior analysis;
  • Education robot and artificial intelligence education;
  • Application of collaborative intelligence in other fields.

We look forward to receiving your contributions.

Prof. Dr. Yong Tang
Prof. Dr. Chaobo He
Dr. Chengzhou Fu
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. Big Data and Cognitive Computing 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

  • social network
  • data intelligence
  • artificial intelligence
  • humanistic computing
  • knowledge graph
  • academic data mining
  • intelligent education

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 1803 KiB  
Article
Exploring Named Entity Recognition via MacBERT-BiGRU and Global Pointer with Self-Attention
by Chengzhe Yuan, Feiyi Tang, Chun Shan, Weiqiang Shen, Ronghua Lin, Chengjie Mao and Junxian Li
Big Data Cogn. Comput. 2024, 8(12), 179; https://doi.org/10.3390/bdcc8120179 - 3 Dec 2024
Viewed by 487
Abstract
Named Entity Recognition (NER) is a fundamental task in natural language processing that aims to identify and categorize named entities within unstructured text. In recent years, with the development of deep learning techniques, pre-trained language models have been widely used in NER tasks. [...] Read more.
Named Entity Recognition (NER) is a fundamental task in natural language processing that aims to identify and categorize named entities within unstructured text. In recent years, with the development of deep learning techniques, pre-trained language models have been widely used in NER tasks. However, these models still face limitations in terms of their scalability and adaptability, especially when dealing with complex linguistic phenomena such as nested entities and long-range dependencies. To address these challenges, we propose the MacBERT-BiGRU-Self Attention-Global Pointer (MB-GAP) model, which integrates MacBERT for deep semantic understanding, BiGRU for rich contextual information, self-attention for focusing on relevant parts of the input, and a global pointer mechanism for precise entity boundary detection. By optimizing the number of attention heads and global pointer heads, our model achieves an effective balance between complexity and performance. Extensive experiments on benchmark datasets, including ResumeNER, CLUENER2020, and SCHOLAT-School, demonstrate significant improvements over baseline models. Full article
(This article belongs to the Special Issue Research Progress in Artificial Intelligence and Social Network Analysis)
►▼ Show Figures

Figure 1

19 pages, 2148 KiB  
Article
Mixture of Attention Variants for Modal Fusion in Multi-Modal Sentiment Analysis
by Chao He, Xinghua Zhang, Dongqing Song, Yingshan Shen, Chengjie Mao, Huosheng Wen, Dingju Zhu  and Lihua Cai
Big Data Cogn. Comput. 2024, 8(2), 14; https://doi.org/10.3390/bdcc8020014 - 29 Jan 2024
Cited by 1 | Viewed by 2343
Abstract
With the popularization of better network access and the penetration of personal smartphones in today’s world, the explosion of multi-modal data, particularly opinionated video messages, has created urgent demands and immense opportunities for Multi-Modal Sentiment Analysis (MSA). Deep learning with the attention mechanism [...] Read more.
With the popularization of better network access and the penetration of personal smartphones in today’s world, the explosion of multi-modal data, particularly opinionated video messages, has created urgent demands and immense opportunities for Multi-Modal Sentiment Analysis (MSA). Deep learning with the attention mechanism has served as the foundation technique for most state-of-the-art MSA models due to its ability to learn complex inter- and intra-relationships among different modalities embedded in video messages, both temporally and spatially. However, modal fusion is still a major challenge due to the vast feature space created by the interactions among different data modalities. To address the modal fusion challenge, we propose an MSA algorithm based on deep learning and the attention mechanism, namely the Mixture of Attention Variants for Modal Fusion (MAVMF). The MAVMF algorithm includes a two-stage process: in stage one, self-attention is applied to effectively extract image and text features, and the dependency relationships in the context of video discourse are captured by a bidirectional gated recurrent neural module; in stage two, four multi-modal attention variants are leveraged to learn the emotional contributions of important features from different modalities. Our proposed approach is end-to-end and has been shown to achieve a superior performance to the state-of-the-art algorithms when tested with two largest public datasets, CMU-MOSI and CMU-MOSEI. Full article
(This article belongs to the Special Issue Research Progress in Artificial Intelligence and Social Network Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop