Name
Abstract | ||
|---|---|---|
As a building block in many graph-based applications, graph partitioning aims to divide a graph into smaller parts of roughly equal size, and meanwhile, minimize the number of cutting edges. Existing solutions for graph partitioning are mainly designed for static graphs and are not appropriate for many dynamic graphs in real-world scenarios, including social networks, knowledge graphs, and web graphs. Although there is an incremental method, called IncKGGGP, proposed to efficiently deal with dynamic graphs, it can only be deployed on top of a specific batch partitioning algorithm, called KGGGP, which inherently impairs the final partitioning quality. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2022.05.096 | Information Sciences |
Keywords | DocType | Volume |
Distributed computing,Dynamic graph,Game theory,Graph partitioning | Journal | 606 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanyuan Zeng | 1 | 0 | 0.34 |
| Yangfan Li | 2 | 0 | 0.68 |
| Xu Zhou | 3 | 221 | 41.36 |
| Jianye Yang | 4 | 0 | 0.34 |
| Wenjun Jiang | 5 | 356 | 24.25 |
| Kenli Li | 6 | 0 | 0.34 |