FENNEL: streaming graph partitioning for massive scale graphs
By Charalampos E. Tsourakakis, Christos Gkantsidis, Bozidar Radunovic, Milan Vojnovic. Web Search and Data Mining, 2014
372 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/1753c2dc85cc40e0a2e8b4a405c1690eab066d8d
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In Computational Theory and Mathematics
In Computer Vision and Pattern Recognition
In Statistical and Nonlinear Physics
Citations in the corpus
- PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, methodology): “As shown in Figure 2, Pregel adopts a traditional edge-cut [61, 68, 72] to evenly assign vertices among machines and provides interaction between vertices by message-passing along edges.”
- Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design (2016, neutral): “Previous works Graph partitioning has attracted a lot of research interests in scientific computing [28, 47, 73], scaling out large-scale computations [20, 59, 60, 141, 147, 155], graph database [39, 122, 144], search and social network analysis [7, 112, 141], and streaming processing [105, 130, 131”
- Recent Advances in Graph Partitioning (2013, methodology): “For details on SGP we refer the reader to [SK12, TGRV00, NU13].”