Toward Real-World Graph Intelligence: A Systematic Journey through Data, Architecture, and Applications

PhD Thesis Proposal Defence


Title: "Toward Real-World Graph Intelligence: A Systematic Journey through 
Data, Architecture, and Applications"

by

Mr. Peiyan ZHANG


Abstract:

The rapid advancement of graph neural networks (GNNs) has revolutionized 
machine learning on relational data, enabling state-of-the-art performance 
in tasks like node classification, link prediction, and graph-level 
inference. By aggregating information from neighboring nodes and edges, GNNs 
effectively integrate local topology and attribute information. However, 
real-world graph intelligence often diverges from controlled benchmarks, 
presenting challenges such as structural variability, domain shifts, 
temporal evolution, and multimodal or incomplete attributes. These 
complexities hinder learning, generalization, and robustness. Additionally, 
existing GNNs struggle to unify local structural information with global 
semantics, limiting their ability to model long-range dependencies, avoid 
over-smoothing, and reason under open-world conditions. In this thesis, we 
address these challenges through innovations in data modeling and 
architectural design. We propose (1) inductive graph alignment prompts for 
cross-graph transfer, (2) high-frequency-aware contrastive coding for 
structural robustness, and (3) parameter isolation for continual learning to 
enable incremental adaptation without catastrophic forgetting. 
Architecturally, we introduce TransGNN, which combines GNNs with 
Transformer-based reasoning to bridge local and global semantics, and 
RAGify, a retrieval-augmented framework that enhances GNN adaptability by 
moving beyond static graph structures. These contributions establish a 
foundation for generalizable, adaptive, and deployment-ready graph learning 
systems.


Date:                   Tuesday, 17 June 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Dr. Yangqiu Song (Supervisor)
                        Prof. Lei Chen (Chairperson)
                        Dr. May Fung