Cascade-aware partitioning of large graph databases

Date
2019
Advisor
Instructor
Source Title
The VLDB Journal
Print ISSN
1066-8888
Electronic ISSN
Publisher
Springer
Volume
28
Issue
3
Pages
329 - 350
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

Graph partitioning is an essential task for scalable data management and analysis. The current partitioning methods utilize the structure of the graph, and the query log if available. Some queries performed on the database may trigger further operations. For example, the query workload of a social network application may contain re-sharing operations in the form of cascades. It is beneficial to include the potential cascades in the graph partitioning objectives. In this paper, we introduce the problem of cascade-aware graph partitioning that aims to minimize the overall cost of communication among parts/servers during cascade processes. We develop a randomized solution that estimates the underlying cascades, and use it as an input for partitioning of large-scale graphs. Experiments on 17 real social networks demonstrate the effectiveness of the proposed solution in terms of the partitioning objectives.

Course
Other identifiers
Book Title
Keywords
Graph partitioning, Information cascade, Propagation models, Randomized algorithms, Scalability, Social networks
Citation
Published Version (Please cite this version)