2014 CoreDecompositionofUncertainGra

• (Bonchi et al., 2014) ⇒ Francesco Bonchi, Francesco Gullo, Andreas Kaltenbrunner, and Yana Volkovich. (2014). “Core Decomposition of Uncertain Graphs.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623655

Subject Headings: Matrix Decomposition, Uncertain Graph.

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Core+Decomposition+of+Uncertain+Graphs
• http://dl.acm.org/citation.cfm?id=2623330.2623655&preflayout=flat#citedby

Quotes

Author Keywords

• Core decomposition; data mining; dense subgraph; graph algorithms; uncertain graphs

Abstract

Core decomposition has proven to be a useful primitive for a wide range of graph analyses. One of its most appealing features is that, unlike other notions of dense subgraphs, it can be computed linearly in the size of the input graph.

In this paper we provide an analogous tool for uncertain graphs, i.e., graphs whose edges are assigned a probability of existence. The fact that core decomposition can be computed efficiently in deterministic graphs does not guarantee efficiency in uncertain graphs, where even the simplest graph operations may become computationally intensive. Here we show that core decomposition of uncertain graphs can be carried out efficiently as well.

We extensively evaluate our definitions and methods on a number of real-world datasets and applications, such as influence maximization and task-driven team formation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 CoreDecompositionofUncertainGra	Francesco Bonchi Francesco Gullo Andreas Kaltenbrunner Yana Volkovich			Core Decomposition of Uncertain Graphs				10.1145/2623330.2623655		2014