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In this paper, we propose to query correlated graphs in a data stream scenario, where an algorithm is required to retrieve the top k graphs which are mostly correlated to a query graph q. Due to the dynamic changing nature of the stream data and the inherent complexity of the graph query process, treating graph streams as static datasets is computationally infeasible or ineffective. In the paper, we propose a novel algorithm, Hoe-PGPL, to identify top-k correlated graphs from data stream, bydoi:10.1145/2396761.2398717 dblp:conf/cikm/PanZ12a fatcat:lugcho6xdbdtzgg5srgcqkwvsa