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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제17권 제2호
- 발행연도
- 2021.1
- 수록면
- 213 - 226 (14page)
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초록· 키워드
오류제보하기
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 respectivelyby using the structural information of paths with the step size of 2 and 3 between the two nodes. Then the totalsimilarity of the node pair can be obtained by combining them. Its absolute value measures the possibility ofthe two nodes to establish a link, and its sign is the sign prediction result of the predicted link. The effectivenessand correctness of the proposed algorithm are verified on six typical datasets. Comparison and analysis are alsocarried out with the classical prediction algorithms in signed networks such as CN-Predict, ICN-Predict, andPSNBS (prediction in signed networks based on balance and similarity) using the evaluation indexes like areaunder the curve (AUC), Precision, improved AUC′, improved Accuracy′, and so on. Results show that theproposed algorithm achieves good performance in both link prediction and sign prediction, and its accuracy ishigher than other algorithms. Moreover, it can achieve a good balance between prediction accuracy andcomputational complexity.
목차
등록된 정보가 없습니다.
참고문헌 (10)
참고문헌 신청
M. M. Liu / 2017 / Link prediction in signed networks based on the similarity and structural balance theory / Journal of Information Hiding and multimedia Signal Processing 8(4):831~846
M. M. Liu / 2020 / Survey of link prediction algorithms in signed networks / Computer Science 47(2):21~30
D. Li / 2019 / Exploiting unlabeled ties for link prediction in incomplete signed networks / Proceedings of 2019 3rd IEEE International Conference on Robotic Computing (IRC) :538~543
X. Su / 2018 / Local labeling features and a prediction method for a signed network / CAAI Transactions on Intelligent Systems 13(3):437~444
P. Shen / 2019 / Unsupervised negative link prediction in signed social networks / Mathematical Problems in Engineering 2019
M. M. Liu / 2020 / Survey of link prediction algorithms in signed networks / Computer Science 47(2):21~30
D. Li / 2019 / Exploiting unlabeled ties for link prediction in incomplete signed networks / Proceedings of 2019 3rd IEEE International Conference on Robotic Computing (IRC) :538~543
X. Su / 2018 / Local labeling features and a prediction method for a signed network / CAAI Transactions on Intelligent Systems 13(3):437~444
P. Shen / 2019 / Unsupervised negative link prediction in signed social networks / Mathematical Problems in Engineering 2019
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