Deep Learning on Real-World Graphs

2025-10-24

AI Summary

Real-world graph data commonly suffer from poor scalability, dynamic evolution, directed heterophily, missing node features, and structural uncertainty—challenging the deployment of Graph Neural Networks (GNNs) in industrial applications such as social networks and recommender systems. To address these multifaceted challenges, we propose five complementary models: SIGN (for scalable static graphs), TGN (for temporal graphs), Dir-GNN (for directed heterophilous graphs), FP (for robust feature propagation under feature absence), and NuGget (for game-theoretic structural inference). Our framework systematically integrates temporal graph modeling, directed graph learning, robust feature propagation, and structure reasoning. It enables efficient training and strong generalization on graphs with millions of nodes. Extensive experiments demonstrate that each model achieves significant improvements over state-of-the-art methods on its respective task, effectively bridging the gap between academic GNN designs and industrial-scale graph requirements.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Addressing scalability limitations in real-world graph neural networks
• Handling temporal dynamics and directionality in graph-structured data
• Overcoming data incompleteness and structural uncertainty in graphs

Innovation

• SIGN model enables scalable graph learning
• TGN handles temporal graph data processing
• Dir-GNN manages directed heterophilic networks