解决真实图数据中可扩展性、时序性等五大难题，让图神经网络更实用。
Deep Learning on Real-World Graphs
- 提出SIGN、TGN等五种模型，分别应对可扩展性、时序图等挑战
- 在真实社交与推荐系统数据上验证性能，显著提升实用性
- 适合工业界部署，推动GNN从学术走向真实场景应用
图神经网络（GNN）已成为图结构数据学习的核心工具，但其在真实世界系统中的应用受限于可扩展性、时序性、方向性、数据不完整性及结构不确定性等关键挑战。本文提出一系列模型以克服这些限制：SIGN用于可扩展图学习，TGN用于时序图建模，Dir-GNN用于有向和异质性网络，特征传播（FP）用于缺失节点特征的学习，NuGget用于博弈论式结构推断。这些方法共同弥合了学术基准与工业级图数据之间的差距，使GNN能够应用于社交系统与推荐系统等实际场景。
原文摘要 · Abstract (English)
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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