Graph Machine Learning and Complex Networks

Dear Colleagues,

Currently, two major research challenges are machine/deep learning and complex networks and both have inter-disciplinary characteristics. Graph machine learning is a novel branch of machine learning that deals with graph-based data to design from expertise, whereas complex networks permit the modeling of large systems through a graph exploiting its formal nature.

Machine and deep learning both assist with a wide range of problems in different areas whilst complex networks are able to model a lot of practical settings, including engineering, neuroscience, social networks, geoscience, economics, etc.

Since complex networks and graph machine learning are closely related, this Special Issue focus on method, strategies, and techniques based on graph machine learning applied to networks to leverage the performance of graph machine learning techniques with high efficiency.

Prof. Dr. Vincenza Carchiolo
Dr. Marco Grassia
Guest Editors

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