A Fast and Inherently Nonnegative Latent Factorization of Tensors Model for Dynamic Directed Network Representation
Author:
Affiliation:
1. Southwest University,College of Computer and Information Science,Chongqing,China
Funder
National Natural Science Foundation of China
Chongqing Municipal Education Commission
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10579968/10579982/10580362.pdf?arnumber=10580362
Reference24 articles.
1. Deep Dynamic Network Embedding for Link Prediction
2. Advancing Non-negative Latent Factorization of Tensors with Diversified Regularizations
3. Time-aware Gradient Attack on Dynamic Network Link Prediction
4. A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis
5. DeepEye: Link prediction in dynamic networks based on non-negative matrix factorization
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