Link Prediction in Evolving Networks Based on Information Propagation
Keywords:
Link Prediction, Common Influence, Similarity Index.Abstract
In graph data mining, link prediction is a major problem. Link prediction is used in social networks to anticipate lost linkages in present networks as well as new ties in future networks. This method has a wide range of applications, including recommender systems, spam mail categorization, and domain expert identification in a variety of research fields. We present a novel model, Common Influence Set, to calculate node similarities in order to forecast future node similarity. The suggested link prediction method calculates a similarity score between two unconnected nodes using the shared influence set of the two nodes. We compared the performance of our method to that of earlier link prediction algorithms based on similarity across a variety of parameters using the area under the ROC curve (AUC).
References
- li>L. A. Adamic and E. Adar, ‘‘Friends and neighbors on the Web,’’ Soc. Netw., vol. 25, no. 3, pp. 211–230, 2003.
- L. Backstrom and J. Leskovec, ‘‘Supervised random walks: Predicting and recommending links in social networks,’’ in Proc. ACM Int. Conf. Web Search Data Mining, 2010, pp. 635–644.
- L. Berton, J. Valverde-Rebaza, and A. de Andrade Lopes,‘‘Link prediction in graph construction for supervised and semi-supervised learning,’’ in Proc. Int. Joint Conf. Neural Netw., 2015, pp. 1–8
- B. Du and L. Zhang, ‘‘A discriminative metric learning based anomaly detection method,’’ IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 6844–6857, Nov. 2014.
- B. Du and L. Zhang, ‘‘Target detection based on a dynamic subspace,’’ Pattern Recognit., vol. 47, no. 1, pp. 344–358, 2014.
- B. Du, S. Cai, C. Wu, L. Zhang, and D. Tao, ‘‘Object tracking in satellite videos based on a multi-frame optical flow tracker,’’ CoRR, vol. abs/1804.09323, 2018. [Online]. Available: https://arxiv.org/ abs/1804.09323
- B. Du, Y. Zhang, L. Zhang, and D. Tao, ‘‘Beyond the sparsity-based target detector: A hybrid sparsity and statistics-based detector for hyperspectral images,’’ IEEE Trans. Image Process., vol. 25, no. 11, pp. 5345–5357, Nov. 2016.
- L.Gao, J. Wu, C. Zhou, and Y. Hu, ‘‘Collaborative dynamic sparse topic regression with user profile evolution for item recommendation,’’ in Proc. 21st Conf. Artif. Intell. (AAAI), 2017, pp. 1–7.
- M. Jiang, Y. Chen, and L. Chen, ‘‘Link prediction in networks with nodes attributes by similarity propagation,’’ CoRR, vol. abs/1502.04380, Feb. 2015. [Online]. Available: https://arxiv.org/abs/1502.04380
- L. Katz, ‘‘A new status index derived from sociometric analysis,’’ Psychometrika, vol. 18, no. 1, pp. 39–43, 1953.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.