Gopher: Efficient Dynamic Graph Pattern Mining via DAG-Driven Execution

Proceedings of the 21st European Conference on Computer Systems 2026 Open access

Read full text
Authors: Yi Zhang, Yu Huang, Chaoqiang Liu, Haifeng Liu, Juntao Chen, Jingrui Yuan, Jianhui Yue, Xiaofei Liao, Hai Jin, Jingling Xue
DOI: 10.1145/3767295.3769365
Cited by: 1
References: 9

Abstract

Graph pattern mining is essential for analyzing dynamic networks, where graphs evolve over time. To accommodate these changes, existing solutions update match sets incrementally, avoiding the need to re-mine the entire graph and achieving significant performance improvements. However, these methods suffer from inefficiencies due to redundant set intersection operations across subgraph instances, causing performance degradation.

Fields of Study & Subjects

Graph Theory and Algorithms Data Mining Algorithms and Applications Advanced Graph Neural Networks

Publication details

Journal / source
Proceedings of the 21st European Conference on Computer Systems
Publisher
ACM
Pages
1722–1737
Publication date
24 April 2026