JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Link Prediction Algorithm for Signed Social Networks Based on Local and Global Tightness |
| 영문제목(English Title) |
Link Prediction Algorithm for Signed Social Networks Based on Local and Global Tightness |
| 저자(Author) |
Miao-Miao Liu
Qing-Cui Hu
Jing-Feng Guo
Jing Chen
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| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0213 ~ 0226 (2021. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
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.
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| 키워드(Keyword) |
Link Prediction
Sign Prediction
Signed Social Networks
Similarity
Structural Balance Theory
Tightness
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| 파일첨부 |
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