2013 RestreamingGraphPartitioningSim

• (Nishimura & Ugander, 2013) ⇒ Joel Nishimura, and Johan Ugander. (2013). “Restreaming Graph Partitioning: Simple Versatile Algorithms for Advanced Balancing.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487696

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Restreaming+Graph+Partitioning%3A+Simple+Versatile+Algorithms+for+Advanced+Balancing
• http://dl.acm.org/citation.cfm?id=2487575.2487696&preflayout=flat#citedby

Quotes

Author Keywords

• Balanced partitioning; computations on discrete structures; graph algorithms; graph clustering; multi-constraint balance; social networks; stratified partitioning

Abstract

Partitioning large graphs is difficult, especially when performed in the limited models of computation afforded to modern large scale computing systems. In this work we introduce restreaming graph partitioning and develop algorithms that scale similarly to streaming partitioning algorithms yet empirically perform as well as fully offline algorithms. In streaming partitioning, graphs are partitioned serially in a single pass. Restreaming partitioning is motivated by scenarios where approximately the same dataset is routinely streamed, making it possible to transform streaming partitioning algorithms into an iterative procedure.

This combination of simplicity and powerful performance allows restreaming algorithms to be easily adapted to efficiently tackle more challenging partitioning objectives. In particular, we consider the problem of stratified graph partitioning, where each of many node attribute strata are balanced simultaneously. As such, stratified partitioning is well suited for the study of network effects on social networks, where it is desirable to isolate disjoint dense subgraphs with representative user demographics. To demonstrate, we partition a large social network such that each partition exhibits the same degree distribution in the original graph --- a novel achievement for non-regular graphs.

As part of our results, we also observe a fundamental difference in the ease with which social graphs are partitioned when compared to web graphs. Namely, the modular structure of web graphs appears to motivate full offline optimization, whereas the locally dense structure of social graphs precludes significant gains from global manipulations.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 RestreamingGraphPartitioningSim	Johan Ugander Joel Nishimura			Restreaming Graph Partitioning: Simple Versatile Algorithms for Advanced Balancing				10.1145/2487575.2487696		2013