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Heterogeneous information networks (e.g. cloud service relation networks and social networks), where multiple-typed objects are interconnected, can be structured by big graphs. A major challenge for clustering in such big graphs is the complex structures that can generate different results, carrying many diverse semantic meanings. In order to generate desired clustering, we propose a parallel clustering method for the heterogeneous information net-works on an efficient graph computation systemdoi:10.1109/access.2019.2910804 fatcat:4itx266235bavpmirqg55ipigi