Scalable Multi-resolution Graph Representation Learning: Towards Knowledge-Enhanced Language Models

Abstract

This thesis presents Scalable Multi-resolution Graph Representation Learning (SMGRL), a novel framework for learning effective representations of large-scale graphs. SMGRL addresses key challenges in existing graph neural network approaches, including computational complexity, capturing long-range dependencies, and adapting to multi-scale graph structures. The framework employs a hierarchical graph coarsening technique that preserves spectral properties, enabling the application of a single graph neural network across multiple resolutions. This multi-resolution approach allows SMGRL to efficiently capture both local and global graph structures while significantly reducing computational requirements. We provide a comprehensive theoretical analysis of SMGRL, including computational complexity, representational capacity, and spectral properties. Extensive experiments on a variety of benchmark datasets demonstrate SMGRL's effectiveness in node classification, link prediction, and graph classification tasks, outperforming state-of-the-art baselines while offering superior scalability. Furthermore, this thesis explores the potential of adapting SMGRL concepts to enhance large language models (LLMs) with structured knowledge from knowledge graphs (KGs). We propose novel architectures and training strategies for integrating multi-resolution KG embeddings with transformer-based language models. Theoretical frameworks for knowledge injection in LLMs are developed, along with potential evaluation methodologies for knowledge-enhanced language models. The thesis concludes by discussing future research directions, including extensions to dynamic and heterogeneous graphs, advanced techniques for aligning graph-based and language-based representations, and ethical considerations surrounding privacy and bias in knowledge-enhanced AI systems. This work contributes to the development of more scalable, knowledgeable, and responsible AI systems, bridging the gap between graph representation learning and natural language processing.