LearnGraph: A Learning-Based Architecture for Dynamic Graph Processing

Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management. While learned indexes improve data structure access, they struggle with interconnected graph data. We present LearnGraph, a novel architecture with an adaptive tree-based memory manager that dynamically optimizes for graph topology and access patterns. Our design integrates two key components: a hierarchical learned index optimized for graph topology to predict vertex and edge locations, and an adaptive tree structure that automatically reorganizes memory regions based on access patterns. Evaluation results demonstrate that LearnGraph outperforms state-of-theart dynamic graph systems, achieving $3.4 \times$ higher throughput on average and reducing processing time by $1.7 \times$ to $11 \times$ across standard graph workloads.