Dear Colleagues,

With the rapid development of modern internet technology, vast quantities of data are generated every day in practice, including texts, videos and images. For instance, the emergence and prosperity of various social platforms have witnessed the rapid development of online communications, especially information sharing.

In the era of big data, the core research fields of both academia and industry reside in data-driven applications, consisting of many machine learning approaches, such as supervised, semi-supervised and unsupervised learning, aiming to mine valuable information from the big data to provide convenience to our daily lives. Especially, graph analysis is becoming a hot topic that attracts various interests from biologists, economists, chemists, physicists, etc. The collected data in graph analysis can be represented by graph data through different embedding approaches, while many mathematics-based methods (e.g., matrix factorization) are developed. Recently, graph neural networks, which originate from spectral graph theory, generalize neural networks and deep learning to the graph. Due to the emergence of various deep learning models, the performance of analyzing data collected from practical systems can be efficiently promoted, such as recommendation, traffic forecasting, medicine development, epidemic spreading and natural language processing. The aim of this Special Issue is to publish cutting-edge original research papers on the latest advances in the analysis and application of big data in the development of machine learning approaches, including theories, models, algorithms, and applications in the real world.

Potential topics of interest include, but are not limited to:

• Machine learning theories;
• Machine learning models;
• Machine learning algorithms;
• Embedding/representation methods;
• Feature selection and clustering;
• Graph neural networks/graph convolutional networks;
• Complex network analysis based on GNNs;
• Sentiment analysis and text classification;
• False account/news detection for online social networks;
• Applications in NLP, emotional analysis, computer vision, intelligent traffic, recommendation system, financial, new medicine design, epidemiologic modeling, etc.

Prof. Dr. Chao Gao
Prof. Dr. Zhen Wang
Prof. Dr. Peican Zhu
Guest Editors

Manuscript Submission Information

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• machine learning
• deep learning
• graph convolutional networks
• statistical analysis of big data
• applications of big data analysis
• data-based complex network analysis