Deep Learning on Real-World Graphs

• URL: http://arxiv.org/abs/2510.21994v1
• Date: Fri, 24 Oct 2025 19:58:13 GMT
• Title: Deep Learning on Real-World Graphs
• Authors: Emanuele Rossi,
• Abstract summary: This thesis introduces a series of models addressing the limitations of graph Neural Networks (GNNs)<n>The contributions bridge the gap between academic benchmarks and industrial-scale graphs, enabling the use of GNNs in domains such as social and recommender systems.
• Score: 3.2658295979028753
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph Neural Networks (GNNs) have become a central tool for learning on graph-structured data, yet their applicability to real-world systems remains limited by key challenges such as scalability, temporality, directionality, data incompleteness, and structural uncertainty. This thesis introduces a series of models addressing these limitations: SIGN for scalable graph learning, TGN for temporal graphs, Dir-GNN for directed and heterophilic networks, Feature Propagation (FP) for learning with missing node features, and NuGget for game-theoretic structural inference. Together, these contributions bridge the gap between academic benchmarks and industrial-scale graphs, enabling the use of GNNs in domains such as social and recommender systems.

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