Name
Title | ||
|---|---|---|
Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs. |
Abstract | ||
|---|---|---|
Distance and centrality computations are important building blocks for modern graph databases as well as for dedicated graph analytics systems. Two commonly used centrality metrics are the compute-intense closeness and betweenness centralities, which require numerous expensive shortest distance calculations. We propose batched algorithm execution to run multiple distance and centrality computations at the same time and let them share common graph and data accesses. Batched execution amortizes the high cost of random memory accesses and presents new vectorization potential on modern CPUs and compute accelerators. We show how batched algorithm execution can be leveraged to significantly improve the performance of distance, closeness, and betweenness centrality calculations on unweighted and weighted graphs. Our evaluation demonstrates that batched execution can improve the runtime of these common metrics by over an order of magnitude. |
Year | Venue | Keywords |
|---|---|---|
2017 | Datenbank-Spektrum | Graph databases, Graph analytics, Closeness centrality, Betweenness centrality |
Field | DocType | Volume |
Network science,Graph database,Random walk closeness centrality,Computer science,Closeness,Vectorization (mathematics),Centrality,Theoretical computer science,Betweenness centrality,Network theory | Journal | 17 |
Issue | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Then | 1 | 43 | 4.73 |
| Stephan G眉nnemann | 2 | 833 | 69.26 |
| Alfons Kemper | 3 | 3519 | 769.50 |
| Thomas Neumann | 4 | 2523 | 156.50 |