Graph Machine Learning and Complex Networks
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".
Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 4707
Special Issue Editors
Interests: network science; natural language processing; data analysis; machine learning; information spread; distributed systems
Special Issues, Collections and Topics in MDPI journals
Interests: geometric deep learning; machine learning; network science; social network
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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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Keywords
- deep generative models for graphs
- geometric deep learning
- graph neural networks
- graph structure of the web
- knowledge graphs
- node embeddings and classification
- application
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