# Deep Learning on Real-World Graphs

arXiv: https://arxiv.org/abs/2510.21994 · HTML: https://mlsanity.com/paper/2510.21994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2025.

## Abstract (arXiv)

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.