2010 DiscoveringFrequentSubgraphsove

Subject Headings: Frequent Subgraph Mining.

Notes

Uncertain graph, frequent subgraph, probabilistic semantics

Frequent subgraph mining has been extensively studied on certain graph data. However, uncertainties are inherently accompanied with graph data in practice, and there is very few work on mining uncertain graph data. this paper investigates frequent subgraph mining on uncertain graphs under probabilistic semantics. Specifically, a measure called [math]\displaystyle{ \varphi }[/math]-frequent probability is introduced to evaluate the degree of recurrence of subgraphs. Given a set of uncertain graphs and two constant value [math]\displaystyle{ 0 \lt \varphi, \tau \lt 1 }[/math], the goal is to quickly find all subgraphs with [math]\displaystyle{ \varphi }[/math]-frequent probability at least [math]\displaystyle{ \tau }[/math]. Due to the NP-hardness of the problem, an approximate mining algorithm is proposed for this problem. Let [math]\displaystyle{ 0 \lt \delta \lt 1 }[/math] be a parameter. The algorithm guarantees to find any frequent subgraph [math]\displaystyle{ S }[/math] with probability at least [math]\displaystyle{ \left (\frac { 1 - \delta } { 2 } \right)^s }[/math], where [math]\displaystyle{ s }[/math] is the number of edges of [math]\displaystyle{ S }[/math]. In addition, it is thoroughly discussed how to set [math]\displaystyle{ \delta }[/math] to guarantee the overall approximation quality of the algorithm. The extensive experiments on real uncertain graph data verify that the algorithm is efficient and that the mining results have very high quality.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 DiscoveringFrequentSubgraphsove	Zhaonian Zou Hong Gao Jianzhong Li			Discovering Frequent Subgraphs over Uncertain Graph Databases under Probabilistic Semantics		KDD-2010 Proceedings		10.1145/1835804.1835885		2010