عنوان انگلیسی مقاله:
Learning deep neural networks for node classification
ترجمه فارسی عنوان مقاله:
یادگیری شبکه های عصبی عمیق برای طبقه بندی گره
Sciencedirect - Elsevier - Expert Systems With Applications, 137 (2019) 324-334: doi:10:1016/j:eswa:2019:07:006
Bentian Li a , Dechang Pi a , b
Deep Neural Network (DNN) has made great leaps in image classification and speech recognition in re- cent years. However, employing DNN for node classification such as in social network remains to be a non-trivial problem. Moreover, the current advanced method of implementing node classification tasks usually takes two steps, i.e. firstly, the embedding vector of the node is obtained through network em- bedding and then the classifier such as SVM is leveraged to do the task. Distinctly, this may only get the suboptimal solution of the problem. To settle the above issues, a novel Deep Neural Network method for node classification named DNNNC is proposed in the framework of Deep Learning. Specifically, we first get the positive pointwise mutual information (PPMI) matrix from the given adjacency matrix. Then, the data is fed to deep neural network composed of deep stacked sparse autoencoders and softmax layer, which could learn the node representation while encoding the rich nonlinear structural and semantic in- formation and could be well trained for node classification under the DNN framework. Extensive experi- ments are conducted on real-world network datasets for node classification task and have shown that the proposed model DNNNC outperforms the state-of-the-art method in the view of superior performance.
Keywords: Network embedding | Node classification | Deep neural network | Deep learning