Abstract
Due to the huge amount of data, network in real world is expanding rapidly. The structure of network is so complicate that a handy tool for analyzing is in desperate need. Link prediction provides forecast about entity interactions. A large range of areas can gain useful insights applying the prediction result. It can also help us understand the evolutionary mechanism of complex networks theoretically. This paper is aimed at promoting the performance of link prediction. We worked on two aspects to meet this end. Firstly, the weight and timestamp information were included in the data we referred to. We brought up the assumption of time attenuation effect. Secondly, we modified the PA index by dividing the links around targeted node pairs into different types. Instead of just focusing on the degree of two targeted nodes, links of various types weighs differently on the final prediction result. We conducted experiments on four real world datasets. The AUC using PA index considering link types was improved indeed.
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This work is supported by National Natural Science Foundation of China (Grant No. 61871046).
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Zhang, X., Wang, X., Zhang, L. (2020). Link Prediction Based on Modified Preferential Attachment for Weighted and Temporal Networks. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_71
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