2026-09-19T06:15:21Z https://api.figshare.com/v2/oai

oai:figshare.com:article/30425152 2025-08-01T00:00:00Z portal_693 item_type_8 month_year_08_2025

Towards Efficient and Scalable Deep Learning on Graph-Structured Data Hengrui Zhang (6167507) Computer Science The practical deployment of Graph Neural Networks (GNNs), a primary form of deep learning on graphs, is hindered by intertwined challenges of effectiveness and scalability. This thesis, "Towards Effective and Scalable Deep Learning on Graph-Structured Data," proposes novel methodologies to address these limitations across four main research thrusts. To address scalability in learning node embeddings, one paper introduces CCA-SSG, a self-supervised framework that learns robust node embeddings. It efficiently avoids the computational burden of negative sampling by using a feature decorrelation objective to prevent representational collapse. To enhance MLP-based models, which are faster but less accurate than GNNs, two papers are presented. OrthoReg tackles an "over-correlation" issue with a soft orthogonality constraint, making MLPs competitive with leading GNNs. The second framework achieves true end-to-end MLP efficiency by offloading graph computations to a one-time pre-processing step, eliminating iterative complexity. Finally, for training on massive graphs, this thesis proposes Data-Centric Graph Condensation (DCGC). This framework recasts condensation as a distribution matching problem, creating a small, task-agnostic synthetic graph. This approach significantly improves cross-architecture generalization and reduces condensation time compared to traditional gradient-matching techniques. The proposed models are validated on public benchmarks, demonstrating significant improvements in performance and computational efficiency. 2025-08-01T00:00:00Z Text Thesis 10.25417/uic.30425152.v1 https://figshare.com/articles/thesis/Towards_Efficient_and_Scalable_Deep_Learning_on_Graph-Structured_Data/30425152 In Copyright