Feb 28, 2015 · Discovery of frequent subgraphs of an input network is one of the most important facilities for mining and analyzing complex networks.
Discovery of frequent subgraphs of an input network is one of the most important facilities for mining and analyzing complex networks.
High Performance Frequent Subgraph Mining on Transaction Datasets
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Apr 4, 2019 · Motoda, An apriori-based algorithm for mining frequent substructures from graph data, in Principles of Data Mining and Knowledge Discovery, ...
◦1st Phase: approximately determine likely frequent subgraphs. »Identify set of subgraphs with high probability. »Collect statistics. »Predict execution time ...
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In this paper, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM adopts a novel task-based ...
In this paper we present gFSG, a computationally efficient algorithm for finding frequent patterns corresponding to geometric subgraphs in a large collection ...
Aug 10, 2021 · In optimization, a subgraph to be found can be “global”, scattered over the whole graph (e.g., vertices with the same color). Moreover, ...
High Performance Computing (HPC) system. It is important to note that ... Karypis, “Frequent Subgraph Discovery,” in Proc. 1st. IEEE Int. Conf. on Data ...
The problem of selecting discriminative molecular fragments in a set of molecules can be formulated in terms of frequent subgraph mining in a set of graphs.
In this paper, we propose two effective strategies to optimize the GraMi algorithm, which help to increase performance as well as reduce memory consumption ...