Constant-time Connectivity Querying in Dynamic Graphs

Connectivity query processing is a fundamental problem in graph processing. Given an undirected graph and two query vertices, the problem aims to identify whether they are connected via a path. Given frequent edge updates in real graph applications, in this paper, we study connectivity query processing in fully dynamic graphs, where edges are frequently inserted or deleted. A recent solution, called D-tree, maintains a spanning tree for each connected component and applies several heuristics to reduce the depth of the tree. To improve the efficiency, we propose a new spanning-tree-based solution by maintaining a disjoint-set tree simultaneously. By combining the advantages of two trees, we achieve the constant query time complexity and also significantly improve the theoretical running time in both edge insertion and edge deletion. Our performance studies on real large datasets show considerable improvement of our algorithms.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Management of Data
Published
2024-12-18
DOI
https://doi.org/10.1145/3698805
Citations
1
Primary Topic
Data Management and Algorithms
Type
article
Field-Weighted Citation Impact
0.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Constant-time Connectivity Querying in Dynamic Graphs

Lu Qin, Lantian Xu, Xuemin Lin, Ying Zhang et al.
1 citations
Proceedings of the ACM on Management of Data
Data Management and Algorithms
0.35
article

Constant-time Connectivity Querying in Dynamic Graphs

Lu Qin, Lantian Xu, Xuemin Lin, Ying Zhang, Rong-Hua Li, Dong Wen
article en
1 citations

Abstract

Connectivity query processing is a fundamental problem in graph processing. Given an undirected graph and two query vertices, the problem aims to identify whether they are connected via a path. Given frequent edge updates in real graph applications, in this paper, we study connectivity query processing in fully dynamic graphs, where edges are frequently inserted or deleted. A recent solution, called D-tree, maintains a spanning tree for each connected component and applies several heuristics to reduce the depth of the tree. To improve the efficiency, we propose a new spanning-tree-based solution by maintaining a disjoint-set tree simultaneously. By combining the advantages of two trees, we achieve the constant query time complexity and also significantly improve the theoretical running time in both edge insertion and edge deletion. Our performance studies on real large datasets show considerable improvement of our algorithms.

Proceedings of the ACM on Management of DataVol. 2(6)
University of Technology Sydney (AU), Beijing Institute of Technology (CN), Shanghai Jiao Tong University (CN), UNSW Sydney (AU)
Openalex Percentile: Top 40%
Data Management and Algorithms
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.