Abstract
From social networks to targeted advertising, big graphs capture the structure in data and are central to recent advances in machine learning and data mining. Unfortunately, directly applying existing data-parallel tools to graph computation tasks can be cumbersome and inefficient. The need for intuitive, scalable tools for graph computation has lead to the development of new graph-parallel systems (e.g., Pregel, PowerGraph) which are designed to efficiently execute graph algorithms. Unfortunately, these new graph-parallel systems do not address the challenges of graph construction and transformation which are often just as problematic as the subsequent computation. Furthermore, existing graph-parallel systems…
Citation impact
583
total citations
- FWCI
- 59.65
- Percentile
- 100%
- References
- 11
Citations per year
Authors
4Topics & keywords
Topics
Keywords
- Computer science
- Scalability
- Wait-for graph
- Graph
- Graph rewriting
- Computation
- Theoretical computer science
- Big data
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Funding
- NSNational Science Foundation
- ICIntel Corporation
- GEGeneral Electric
- MMicrosoft
- CSCisco Systems
- OOracle
- SNSAP North America
- FFacebook
- GGoogle
- AWAmazon Web Services
- HTHuawei Technologies
- DFDirectorate for Computer and Information Science and Engineering
- DADefense Advanced Research Projects AgencyAward: FA8750-12-2-0331
- SSamsung
- DODivision of Computing and Communication FoundationsAward: CCF-1139158