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Graph data are subject to uncertainties in many applications due to incompleteness and imprecision of data. Mining uncertain graph data is semantically different from and computationally more challenging than mining exact graph data. This paper investigates the problem of mining frequent subgraph patterns from uncertain graph data. The frequent subgraph pattern mining problem is formalized by designing a new measure called expected support. An approximate mining algorithm is proposed to find andoi:10.1145/1645953.1646028 dblp:conf/cikm/ZouLGZ09 fatcat:oqeaypns5rga7f2nsogb7am2ey