Abstract
AbstractReal world complex networks are indirect representation of complex systems. They grow over time. These networks are fragmented and raucous in practice. An important concern about complex network is link prediction. Link prediction aims to determine the possibility of probable edges. The link prediction demand is often spotted in social networks for recommending new friends, and, in recommender systems for recommending new items (movies, gadgets etc) based on earlier shopping history. In this work, we propose a new link prediction algorithm namely “Common Neighbor and Centrality based Parameterized Algorithm” (CCPA) to suggest the formation of new links in complex networks. Using AUC (Area Under the receiver operating characteristic Curve) as evaluation criterion, we perform an extensive experimental evaluation of our proposed algorithm on eight real world data sets, and against eight benchmark algorithms. The results validate the improved performance of our proposed algorithm.
Publisher
Springer Science and Business Media LLC
Reference32 articles.
1. Newman, M. E. The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003).
2. Zhou, T., Lü, L. & Zhang, Y.-C. Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009).
3. Liao, H., Zeng, A. & Zhang, Y.-C. Predicting missing links via correlation between nodes. Phys. A: Stat. Mech. its Appl. 436, 216–223 (2015).
4. Al Hasan, M., Chaoji, V., Salem, S. & Zaki, M. Link prediction using supervised learning. In SDM06: workshop on link analysis, counter-terrorism and security (2006).
5. Kamath, P. S. et al. A model to predict survival in patients with end-stage liver disease. Hepatol. 33, 464–470 (2001).
Cited by
74 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献