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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제13권 제4호
- 발행연도
- 2017.1
- 수록면
- 914 - 927 (14page)
이용수
초록· 키워드
오류제보하기
Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methodsbased on local structure are widely used to handle the predictive task. However, the results are still far fromsatisfied as major literatures neglect two important points: common neighbors produce different influence onpotential links; weighted values associated with links in local structure are also different. In this paper, weadapt an effective link prediction model—local naive Bayes model into a weighted scenario to address thisissue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link predictionframework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowingour model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensivelybe applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighteddatasets. Experimental results show that our proposed approach performs better (by AUC and Prec) thanseveral alternative methods for link prediction in weighted complex networks.
목차
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참고문헌 (25)
참고문헌 신청
A. Barrat / 2004 / The architecture of complex weighted networks / Proceedings of the National Academy of Sciences of the United States of America 101 (11) : 3747 ~ 3752
A. Clauset / 2008 / Hierarchical structure and the prediction of missing links in networks / Nature 453 (7191) : 98 ~ 101
A. L. Barabasi / 2002 / Linked: How Everything is Connected to Everything Else and What It Means for Business, Science, and Every Life / Penguin Group
A. Zarezade / / Correlated cascades: compete or cooperate
D. K. Wind / 2012 / Link prediction in weighted networks / Proceedings of 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) : 1 ~ 6
A. Clauset / 2008 / Hierarchical structure and the prediction of missing links in networks / Nature 453 (7191) : 98 ~ 101
A. L. Barabasi / 2002 / Linked: How Everything is Connected to Everything Else and What It Means for Business, Science, and Every Life / Penguin Group
A. Zarezade / / Correlated cascades: compete or cooperate
D. K. Wind / 2012 / Link prediction in weighted networks / Proceedings of 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) : 1 ~ 6
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