نوع مقاله : مقاله پژوهشی
نویسندگان
دانشکده مهندسی برق و کامپیوتر، دانشگاه کاشان، کاشان، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
With the acceleration of the development of science and the publication of articles and the increase of scientific fields, finding suitable research partners, finding research sources and research fields for researchers and relevant institutions is becoming more and more difficult. By choosing these things correctly, you can get the most efficiency from the cost and time spent on research. To solve this problem, a scientific network can be created by creating a network including articles, scientists, and other scientific entities and the connections between them, and predicting the connections that will be formed in the future using link prediction. In this paper, a framework based on machine learning is presented for link prediction in scientific networks. In this framework, by weighting the network based on time and content, calculating embedded structural and textual features, feature selection and extraction, and finally negative sampling using clustering, a machine learning model is trained for link prediction. Each of the steps of this framework was tested separately and all together, and the results showed that the proposed weighting method for the network of references and authors' collaboration increases the accuracy of the weighted similarity criteria and, as a result, increases the accuracy of the entire algorithm. Also, negative sampling using clustering makes the machine learning algorithm better trained. The textual features of scientific data such as the title and abstract of articles also play an effective role in predicting future links.
کلیدواژهها [English]
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