From Chaos to Harmony: Addressing Data De-Noising, Complexity and Adaptability in Graph Machine Learning

Graph representation learning—especially via graph neural networks (GNNs)—has demonstrated considerable promise in modeling intricate interaction systems, such as social networks and molecular structures. However, the deployment of GNN-based frameworks in industrial settings remains challenging due to the inherent complexity and noise in real-world graph data. This dissertation systematically addresses these challenges by advancing novel methodologies to improve the comprehensiveness and robustness of graph representation learning, with a dual focus on resolving data complexity and denoising across diverse graph-learning scenarios. In addressing graph data denoising, we design auxiliary self-supervised optimization objectives that disentangle noisy topological structures and misinformation while preserving the representational sufficiency of critical graph features. These tasks operate synergistically with primary learning objectives to enhance robustness against data corruption. The efficacy of these techniques is demonstrated through their application to real-world opioid prescription time series data for predicting potential opioid over-prescription. To mitigate data complexity, the study investigates two complementary approaches: (1) multimodal fusion, which employs attentive integration of graph data with features from other modalities, and (2) hierarchical substructure mining, which extracts semantic patterns at multiple granularities to enhance model generalization in demanding contexts. Finally, the dissertation explores the adaptability of graph data in a range of practical applications, including E-commerce demand forecasting and recommendations, to further enhance prediction and reasoning capabilities.

图表示学习——尤其是基于图神经网络（Graph Neural Networks, GNNs）的相关技术——在建模复杂交互系统（如社交网络与分子结构）领域已展现出可观的应用潜力。然而，受限于真实世界图数据固有的复杂性与噪声问题，基于图神经网络的框架在工业场景中的部署仍面临诸多挑战。本论文通过提出新颖的方法论系统性地应对上述挑战，旨在提升图表示学习的全面性与鲁棒性，核心聚焦于在多样化的图学习场景中解决数据复杂性问题并实现数据去噪。针对图数据去噪问题，本研究设计了辅助自监督优化目标，可在保留关键图特征表征充分性的同时，解耦含噪拓扑结构与错误信息。此类任务与主学习任务协同运作，能够提升模型对抗数据损坏的鲁棒性。上述技术的有效性，通过应用于真实世界阿片类药物处方时序数据以预测潜在阿片类药物过度处方场景得到了验证。为缓解数据复杂性问题，本研究探索了两种互补的解决方案：（1）多模态融合：通过注意力机制实现图数据与其他模态特征的融合集成；（2）层级子结构挖掘：提取多粒度语义模式，以提升模型在复杂场景下的泛化能力。最后，本论文还探索了图数据在多种实际场景中的适配性，涵盖电子商务需求预测与推荐系统等领域，以进一步提升模型的预测与推理能力。