DeepEye: Link prediction in dynamic networks based on non-negative matrix factorization | Zendy

Nahla Mohamed Ahmed | Zendy; Ling Chen | Zendy; Yulong Wang | Zendy; Bin Li | Zendy; Yun Li | Zendy; Wei Liu | Zendy

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DeepEye: Link prediction in dynamic networks based on non-negative matrix factorization

Author(s) -

Nahla Mohamed Ahmed,

Ling Chen,

Yulong Wang,

Bin Li,

Yun Li,

Wei Liu

Publication year - 2018

Publication title -

big data mining and analytics

Language(s) - English

Resource type - Journals

ISSN - 2096-0654

DOI - 10.26599/bdma.2017.9020002

Subject(s) - correctness , non negative matrix factorization , computer science , construct (python library) , convergence (economics) , representation (politics) , matrix decomposition , algorithm , link (geometry) , matrix (chemical analysis) , factorization , artificial neural network , dynamic network analysis , artificial intelligence , data mining , computer network , eigenvalues and eigenvectors , physics , materials science , quantum mechanics , composite material , politics , political science , law , economics , programming language , economic growth

A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to construct matrix factors that carry important network features and prove the convergence and correct...

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