Streaming graph partitioning for large distributed graphs

By Isabelle Stanton, Gabriel Kliot. Knowledge Discovery and Data Mining, 2012

488 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/5decb5a4a0007fab8fe8cf859f3c56162601c5a7

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs (2012, unknown): “Stanton et al, in [35] developed several heuristics for the streaming edge-cuts but do not consider the vertex-cut problem.”
GPS: a graph processing system (2013, background): “[33] develops a heuristic to partition the graph across machines during the initial loading phase.”
AliGraph: A Comprehensive Graph Neural Network Platform (2019, methodology): “In our system, as recommended in [13], we implement four built-in graph partition algorithms: 1) METIS [27]; 2) Vertex cut and edge cut partitions [16]; 3) 2-D partition [3]; and 4) Streaming-style partition strategy [45].”
PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, background): “Graph replication and partitioning: Generally, prior graph partitioning approaches can be categorized into vertex-cut [24, 31] and edge-cut [49, 61, 68, 72] according to their partition mechanism.”
GraphChi: Large-Scale Graph Computation on Just a PC (2012, unknown): “A recently published paper [42] reports that Spark [48], running on a cluster of 50 machines (100 CPUs) [48] runs”
Gemini: A Computation-Centric Distributed Graph Processing System (2016, unknown): “A number of partitioning methods have been proposed, including both vertex-centric [30, 32, 48] and edge-centric (aka.”
Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design (2016, unknown): “Our contribution Even though partitioning problems are often NP hard [131], for the practical importance of this problem, it is worth seeking scalable solutions that outperform random partitioning.”
From "Think Like a Vertex" to "Think Like a Graph" (2013, background): “Although there has been a lot of work on single-node sequential/parallel graph partitioning algorithms [12, 11, 21], the rapid growth of graph data demands scalable distributed graph partitioning solutions.”
Recent Advances in Graph Partitioning (2013, unknown): “For details on SGP we refer the reader to [160,203,209].”