MACHINE LEARNING FOR TEMPORAL HETEROGENEOUS GRAPHS: PREDICTIVE METHODS, INTERPRETABILITY AND APPLICATIONS.

This thesis develops a coherent framework for designing, evaluating, and interpreting predictive methods for temporal heterogeneous graphs by integrating modeling and tools from network science and graph deep learning. We introduce discrete-time graph learning architectures based on Graph Neural Networks (GNNs) and linear scoring functions tailored for forecasting dynamic, multi-relational networks, and provide a principled extension of message-passing paradigms to this setting. To address the limited interpretability of temporal graph models, we propose leveraging tools from temporal network analysis, such as link prediction heuristics, and we systematically benchmark explainability techniques in evolving relational contexts. Finally, we contribute novel high-resolution datasets derived from Web3 social platforms, enabling several applications for temporal graph learning in this context, such as link recommendation with textual content, transaction predictions, and user migration analysis. Together, these contributions advance both the theoretical foundations and practical applications of machine learning on temporal heterogeneous networks.

Grantor dc:publisher

Università degli Studi di Milano

Year dc:date

2025

Author dc:creator

• DILEO, MANUEL

Contributors dc:contributor

• tutor: M. Zignani
• S. Gaito ; coordinatore: R. Sassi
• M. Dileo
• ZIGNANI, MATTEO
• SASSI, ROBERTO

• machine learning
• graph analysi
• graph machine learning
• artificial intelligence
• temporal graph
• social network analysi
• link prediction
• explainability
• Settore INFO-01/A - Informatica

Statement dc:rights

• info:eu-repo/semantics/openAccess
• license:Creative commons
• license uri:http://creativecommons.org/licenses/by-sa/4.0/

Language dc:language

eng

Identifier

http://dx.doi.org/10.13130/dileo-manuel_phd2025
10.13130/dileo-manuel_phd2025

OAI identifier oai:identifier

oai:air.unimi.it:2434/1195896