ComRank: community-based ranking approach for heterogeneous information network analysis and mining Online publication date: Fri, 16-Oct-2020
by Phu Pham; Phuc Do
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 17, No. 4, 2020
Abstract: In this paper, we propose the ComRank model to address this problem of ranking a specific typed of object, over the generated topic-driven communities in the information networks. The topic-driven communities are generated by applying the latent topic modelling of LDA. Our proposed ComRank model is directly generated ranking results for specific typed object in the different network communities. We apply our approach to construct the scholastic recommendation system, which support the researchers to find the appropriate citations or potential authors for cooperating while doing scientific researches. The ComRank model is tested with the real-world dataset of DBLP bibliographic network. The experimental results demonstrated that our proposed model can generate the meaningful ranking results within detected topic-driven communities.
Online publication date: Fri, 16-Oct-2020
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