Dear Colleagues,

In the last decade, online social networks (OSNs) such as Facebook, Twitter, and Instagram have more and more become an essential part of our life. In particular, the growth of data due to the continuous usage of such networks has focused attention on the development of novel approaches for their effective and efficient analysis. Novel methodologies and techniques are, thus, required to analyze and understand the different social ‘‘ties’’ among users within such communities together with the exchanged information for a large number of applications (e.g., viral marketing, expert finding, community detection, influence or spread diffusion analysis, and so on).

They belong to the “umbrella” term of social network analysis (SNA), whose aim is to investigate social structures using different techniques, considering both social entities’ behavior and their connections. Indeed, these data have a high-dimensional, irregular, and complex structure that can be naturally represented by a graph; thus, in this Special Issue, we focus on graph-based approaches, methods. and tools for SNA which can be exploited for different domains in addition to classical OSNs as well as cultural heritage, e-health, scientific databases, software repositories, etc. In particular, we are interested in the application of graph machine learning techniques for large social network learning and understanding. The aim is to gather solutions, but also lessons learnt, methodologies, and good practices, that researchers and practitioners can use as a basis for their own work concerning the Special Issue topics.

Relevant Topics: Relevant topics of this Special Issue include any aspect of machine learning over graphs, especially for social network analysis. Indicative topics include (but are not restricted to) the following:

• Machine learning over graphs: kernel-based techniques, clustering methods, scalable algorithms; 
• Graph neural networks: convolutional, attention, recurrent; 
• Graph embedding and learning;
• Knowledge graph representation;
• Graph machine learning for social network analysis and social computing;
• Graph machine learning for expert finding and community detection;
• Graph machine learning for social recommendation;
• Graph machine learning for viral marketing;
• Graph machine learning for virus spreading analysis;
• Graph machine learning for e-health applications;
• Graph machine learning for cultural heritage;
• Graph machine learning for software repository mining.

Prof. Dr. Vincenzo Moscato
Dr. Giancarlo Sperlì
Guest Editors

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• graph machine learning
• social network analysis
• graph data mining
• graph-powered machine learning
• graph learning