Any scientific activity requires awareness of previous related activities. Citation networks are the networks in which each document is compared as a link of a chain with its previous and next documents, and the documents with the highest number of citations are considered as the most effective ones in a domain. Most of the introduced methods use direct citations for valuing the documents. One of the challenges in ranking documents are indirect citations and determining effective features to compute the ranks. The presented method uses not only direct but also indirect citations for the valuing process. In this research, several measures for analyzing citation networks are introduced. The combination of these measures and using a data fusion method will improve the way by which documents are valued and ranked. In the presented method, an ordered weighted averaging fusion method is used to determine values based on the direct and indirect citations computed by some defined measures. Through some experiments, the presented method has been compared with some other fusion methods, which results indicate the effectiveness of our method.
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Salkhordeh Haghighi, M., & Sajedi, F. (2021). Valuing Indirect Citations in Citation Networks using Data Fusion. Soft Computing Journal, 7(2), 36-46.
MLA
Mehdi Salkhordeh Haghighi; Fatemeh Sajedi. "Valuing Indirect Citations in Citation Networks using Data Fusion". Soft Computing Journal, 7, 2, 2021, 36-46.
HARVARD
Salkhordeh Haghighi, M., Sajedi, F. (2021). 'Valuing Indirect Citations in Citation Networks using Data Fusion', Soft Computing Journal, 7(2), pp. 36-46.
VANCOUVER
Salkhordeh Haghighi, M., Sajedi, F. Valuing Indirect Citations in Citation Networks using Data Fusion. Soft Computing Journal, 2021; 7(2): 36-46.
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