Journal of Information Processing Systems

  • 제17권2호
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  • Pages.213-226
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  • 2021
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Link Prediction Algorithm for Signed Social Networks Based on Local and Global Tightness

  • Liu, Miao-Miao (Dept. of Computer Science, School of Computer and Information Technology, Northeast Petroleum University) ;
  • Hu, Qing-Cui (Dept. of Computer Science, School of Computer and Information Technology, Northeast Petroleum University) ;
  • Guo, Jing-Feng (Dept. of Computer Science and Engineering, College of Information Science and Engineering, Yanshan University) ;
  • Chen, Jing (Dept. of Computer Science and Engineering, College of Information Science and Engineering, Yanshan University)
  • 투고 : 2020.11.07
  • 심사 : 2020.12.04
  • 발행 : 2021.04.30
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초록

Given that most of the link prediction algorithms for signed social networks can only complete sign prediction, a novel algorithm is proposed aiming to achieve both link prediction and sign prediction in signed networks. Based on the structural balance theory, the local link tightness and global link tightness are defined respectively by using the structural information of paths with the step size of 2 and 3 between the two nodes. Then the total similarity of the node pair can be obtained by combining them. Its absolute value measures the possibility of the two nodes to establish a link, and its sign is the sign prediction result of the predicted link. The effectiveness and correctness of the proposed algorithm are verified on six typical datasets. Comparison and analysis are also carried out with the classical prediction algorithms in signed networks such as CN-Predict, ICN-Predict, and PSNBS (prediction in signed networks based on balance and similarity) using the evaluation indexes like area under the curve (AUC), Precision, improved AUC', improved Accuracy', and so on. Results show that the proposed algorithm achieves good performance in both link prediction and sign prediction, and its accuracy is higher than other algorithms. Moreover, it can achieve a good balance between prediction accuracy and computational complexity.

키워드

과제정보

This work was supported by the Natural Science Foundation of China (No. 42002138 and 61871465), Natural Science Foundation of Heilongjiang Province (No. LH2019F042 and LH2020F003), Youth Science Foundation of Northeast Petroleum University (No. 2018QNQ-01), and Science & Technology Program of Hebei (No. 20310301D).

참고문헌

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