posted by user: drfxia || 9571 views || tracked by 17 users: [display]

TNNLS-GL 2023 : IEEE Transactions on Neural Networks and Learning Systems Special Issue on Graph Learning


When N/A
Where N/A
Submission Deadline Jul 1, 2023
Categories    artificial intelligence   machine learning   data mining   computational intelligence

Call For Papers


Early submissions are encouraged/preferred.

IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)
Special Issue on Graph Learning

Yongduan Song, Chongqing University, China

[Guest Editors]
- Feng Xia, RMIT University, Australia
- Renaud Lambiotte, University of Oxford, United Kingdom
- Neil Shah, Snap Research, USA
- Hanghang Tong, University of Illinois Urbana-Champaign, USA
- Irwin King, The Chinese University of Hong Kong, Hong Kong

Graphs (or networks) are a powerful data structure. The vast majority of real-world scenarios involve graphs, for instance, social networks, traffic networks, neural networks, biological networks, communication networks, and knowledge graphs, just to name a few. However, classical deep learning and machine learning algorithms cannot be directly applied to many graph-based domains due to the characteristics of graph data that lie in an irregular domain (i.e., non-Euclidean space).

Graph learning (a.k.a. graph machine learning or machine learning on graphs) has attracted huge research attention over the past few years thanks to its great potential. For example, graph learning brings the advantageous and significant ability to exploit the topological structure of graphs. Moreover, graph learning can recursively aggregate information from nodes’ neighbours to learn the feature vector of all nodes. The use of graph learning methods, such as graph neural networks, network embedding, representation learning, have led to unprecedented progress in solving many challenges facing real-world applications, such as recommender systems, anomaly detection, smart surveillance, traffic forecasting, disease control and prevention, medical diagnosis, and drug discovery. Despite rapid emergence and significant advancement, the field of graph learning is facing various challenges deriving from, e.g., fundamental theory and models, algorithms and methods, supporting tools and platforms, and real-world deployment and engineering.

This special issue will feature the most recent research results in graph learning. The issue welcomes both theoretical and applied research. It will encourage the effort to share data, advocate gold-standard evaluation among shared data, and promote the exploration of new directions.

[Scope of the Special Issue]
Topics of interest includes (but not limited to):
- Foundations and principles of graph learning
- Novel machine learning models and algorithms over graphs
- Graph neural networks
- Deep learning on graphs
- Graph mining and analytics
- Network representation learning
- Learning on temporal, large-scale, and/or complex graphs
- Responsible and trustworthy graph learning
- Knowledge-informed graph learning
- Robustness and adversarial attacks on graphs
- Geometric machine learning
- Graph theory and network science for machine learning
- Knowledge graphs
- Graph datasets and benchmarks
- Graph learning systems, platforms, and applications in various domains

[Submission Instructions]
- Read the Information for Authors at
- Submit your manuscript through ScholarOne Manuscripts ( and choose “Special Issue: Graph Learning” as Type in Step 1: Type, Title, & Abstract.
- Early submissions are encouraged/preferred. We will start the review process as soon as we receive a submission.

Related Resources

ICDM 2024   24th Industrial Conference on Data Mining
ICANN 2024   33rd International Conference on Artificial Neural Networks
ACM-Ei/Scopus-CCISS 2024   2024 International Conference on Computing, Information Science and System (CCISS 2024)
JCICE 2024   2024 International Joint Conference on Information and Communication Engineering(JCICE 2024)
CVIPPR 2024   2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
MLANN 2024   2024 2nd Asia Conference on Machine Learning, Algorithms and Neural Networks (MLANN 2024)
IEEE Xplore-Ei/Scopus-CVIV 2024   2024 6th International Conference on Advances in Computer Vision, Image and Virtualization (CVIV 2024) -EI Compendex
MLDM 2024   20th International Conference on Machine Learning and Data Mining
25th EANN/EAAAI 2024   25th Engineering Applications of Neural Networks-EAAAI Engineering Applications & Advances of AI
IEEE ACIRS 2024   IEEE--2024 the 9th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS 2024)