Fusing heterogeneous information for multi-modal attributed network embedding
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Zhejiang Province
Natural Science Foundation of Ningbo
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04675-5.pdf
Reference48 articles.
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2. Liang J, Jacobs P, Sun J, Parthasarathy S (2018) Semi-supervised embedding in attributed networks with outliers. In: Proceedings of the 2018 SIAM International Conference on Data Mining, San Diego, pp. 153 – 161 . https://doi.org/10.1137/1.9781611975321.18
3. Bandyopadhyay S, Vivek SV, Murty MN (2020) Outlier resistant unsupervised deep architectures for attributed network embedding. In:Proceedings of the international conference on web search and data mining, Houston, pp 25 – 33
4. Zhao Z, Li C, Zhang X, Chiclana F, Viedma EH (2019) An incremental method to detect communities in dynamic evolving social networks. Knowledge-based systems 163:404 – 415
5. Liu J, Shi C, Yang C, Lu Z, Philip SY (2022) A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources. AI Open 3:40 – 57
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