FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
作者： Tomasz Kajdanowicz , Nitesh V. Chawla , Maciej Falkiewicz , Kamil Tagowski , Piotr Bielak
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摘要: Abstract Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances …
arxiv.org参考文章(39)
Kevin P. Murphy, Machine Learning : A Probabilistic Perspective ,(2012)
Tomas Mikolov, Greg S. Corrado, Kai Chen, Jeffrey Dean, Efficient Estimation of Word Representations in Vector Space international conference on learning representations. ,(2013)
Qiaozhu Mei, Meng Qu, Mingzhe Wang, Jian Tang, Ming Zhang, Jun Yan, LINE: Large-scale Information Network Embedding the web conference. pp. 1067- 1077 ,(2015) , 10.1145/2736277.2741093
Sam T Roweis, Lawrence K Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding Science. ,vol. 290, pp. 2323- 2326 ,(2000) , 10.1126/SCIENCE.290.5500.2323
Mikhail Belkin, Partha Niyogi, Laplacian Eigenmaps for dimensionality reduction and data representation Neural Computation. ,vol. 15, pp. 1373- 1396 ,(2003) , 10.1162/089976603321780317
Daniel Yamins, David Cox, James Bergstra, Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures international conference on machine learning. pp. 115- 123 ,(2013)
Danielle J. Marceau, Luc Guindon, Mireille Bruel, Claude Marois, Building Temporal Topology in a GIS Database to Study the Land-Use Changes in a Rural-Urban Environment The Professional Geographer. ,vol. 53, pp. 546- 558 ,(2001) , 10.1111/0033-0124.00304
Yoav Goldberg, Omer Levy, word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv: Computation and Language. ,(2014)
Ryan A. Rossi, Nesreen K. Ahmed, The network data repository with interactive graph analytics and visualization national conference on artificial intelligence. pp. 4292- 4293 ,(2015)
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu, Asymmetric Transitivity Preserving Graph Embedding knowledge discovery and data mining. pp. 1105- 1114 ,(2016) , 10.1145/2939672.2939751