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 nov...

Full description

Saved in:

Bibliographic Details
Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Zhang, Lingling, Wu, Yijian, Jiang, Hong, Zhou, Ziyu, Lu, Tiancheng
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Adaptation models Data models Indexes Organizations Performance gain Predictive models Standards organizations Systems architecture Throughput Topology
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
DOI:	10.1109/DAC63849.2025.11133339