Journal
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
Volume 8, Issue 1, Pages 171-178Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2020.2987846
Keywords
Correlation mining; cyber intelligence; heterogeneous big data; hierarchical hybrid network (HHN); reinforcement learning; social influence
Funding
- National Key Research and Development Program of China [2017YFE0117500]
- Natural Science Foundation of Hunan Province of China [2019JJ40150]
- Hunan Provincial Education Department Foundation for Excellent Youth Scholars [17B146]
- Key Project of Hunan Provincial Education Department [17A113]
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This article introduces a method for deep correlation mining in big data environments, utilizing a hierarchical hybrid network model, intelligent router, and recommendation mechanism to uncover relationships among entities and support collaborative work. Experiments based on DBLP and ResearchGate data demonstrate the practicality and effectiveness of the model and method.
The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterogeneous big data environments. A hierarchical hybrid network (HHN) model is constructed to describe multitype relationships among different entities, and a series of measures are defined to quantify the internal correlations within one specific layer or external correlations between different layers. An intelligent router based on deep reinforcement learning framework is designed to generate optimal actions to route across the HHN. An improved random walk with the restart-based algorithm is then developed with the intelligent router, based on the hierarchical influence across network associated with multiple correlations. An intelligent recommendation mechanism is finally designed and applied to support users' collaboration works in scholarly big data environments. Experiments based on DBLP and ResearchGate data show the practicability and usefulness of our model and method.
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