The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Streaming graph partitioning for large distributed graphs
Abstract
Extracting knowledge by performing computations on graphs is becoming increasingly challenging as graphs grow in size. A standard approach distributes the graph over a cluster of nodes, but performing computations on a distributed graph is expensive if large amount of data have to be moved. Without partitioning the graph, communication quickly becomes a limiting factor in scaling the system up. Existing graph partitioning heuristics incur high computation and communication cost on large graphs, sometimes as high as the future computation itself. Observing that the graph has to be loaded into the cluster, we ask if the partitioning can be done at the same time with a lightweight streaming algorithm.
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Metadata
- Authors
- Stanton, I., & Kliot, G.
- Venue
- Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2012-08-12
- Pages
- 1222-1230
- OpenAlex
- W1971630691
- Cited by
- 446
- References
- 21
- FWCI
- 23.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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