Intelligent Perception Computing and Graph Neural Networks: Algorithms, Applications, and New Challenges

Dear Colleagues,

We invite you to submit your latest applied research in the field of intelligent perception and graph neural network algorithms to this Special Issue, entitled “Intelligent Perception Computing and Graph Neural Networks: Algorithms, Applications, and New Challenges”.

Intelligent perception has become increasingly crucial in various domains, including computer vision, sensor networks, and language education. Despite significant advancements, numerous challenges remain to be overcome in intelligent sensing, such as the handling of complex and heterogeneous data, efficiently processing large-scale information, and adapting to dynamic environments. In particular, wireless intelligent perception technology faces issues related to signal interference, resource allocation, and real-time processing.

Graph neural networks (GNNs) have emerged as a powerful tool for addressing these challenges in intelligent perception. By leveraging the inherent graph structure of data, GNNs can effectively capture the complex relationships and dependencies among entities. They have demonstrated superior performance in tasks such as object detection, scene comprehension, and signal classification. The ability of GNNs to model the intricate interactions between nodes and edges enables them to extract rich features and make accurate predictions, even in the presence of noise and uncertainty.

Despite the promising potential of GNNs in solving intelligent perception challenges, several open issues and limitations still need to be addressed. Graph neural networks often suffer from high computational complexity, making them difficult to deploy in resource-constrained scenarios. The efficiency of GNNs, both in terms of time and memory, is a critical concern for real-time applications. Moreover, the robustness of GNNs to adversarial attacks and data perturbations requires further investigation. Parallel computing techniques need to be explored to scale GNNs to large-scale datasets and enable their deployment in distributed systems.

To tackle these challenges and advance the state-of-the-art in graph neural networks for intelligent perception computing, we are organizing a Special Issue entitled "Intelligent Perception Computing and Graph Neural Networks: Algorithms, Applications, and New Challenges". We invite researchers and practitioners to submit original research articles, review papers, and case studies that explore novel algorithms, applications, and challenges in this field.

Topics of interest include, but are not limited to, the following:

• Emerging GNN techniques for intelligent perception computing;
• High-performance GNN-based algorithms for intelligent wireless perception technology;
• Efficient and scalable GNN algorithms for large-scale data processing;
• Robustness and adversarial defense mechanisms for GNNs;
• Parallel and distributed computing techniques for GNNs;
• GNN-based methods for sensor fusion and multi-modal data integration;
• Applications of GNNs in computer vision, sensor networks, and wireless communication;
• Theoretical analysis and understanding for intelligent perception scenarios, e.g., target localization, activity recognition, and posture estimation;
• Benchmarking and evaluation frameworks for intelligent perception;
• Solutions toward security, cryptography, and data privacy issues in intelligent perception;
• New challenges and future directions in GNN-based intelligent perception computing.

We encourage submissions that showcase innovative ideas, rigorous methodologies, and significant practical impacts. All submitted papers will undergo a thorough peer-review process to ensure they are of the highest quality and relevance to this Special Issue’s theme.

Dr. Huakun Huang
Dr. Chunhua Su
Dr. Zhuotao Lian
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. Mathematics 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 2600 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.

• graph neural networks
• intelligent perception computing
• parallel and distributed computing
• multi-modal data integration
• computer vision
• sensor networks
• wireless communication
• data privacy