Learning deep neural networks for node classification

•Propose a novel deep neural network method for node classification.•The model could overcome the existing problem of only getting the suboptimal solution.•A superior performance of results demonstrates the effectiveness of proposed approach. Deep Neural Network (DNN) has made great leaps in image c...

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Veröffentlicht in:Expert systems with applications Jg. 137; S. 324 - 334
Hauptverfasser: Li, Bentian, Pi, Dechang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 15.12.2019
Elsevier BV
Schlagworte:
Artificial neural networks
Classification
Deep learning
Deep neural network
Embedding
Image classification
Machine learning
Network embedding
Neural networks
Node classification
Nodes
Object recognition
Social networks
Speech recognition
Deep learning
Deep neural network
Network embedding
Node classification
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•Propose a novel deep neural network method for node classification.•The model could overcome the existing problem of only getting the suboptimal solution.•A superior performance of results demonstrates the effectiveness of proposed approach. Deep Neural Network (DNN) has made great leaps in image classification and speech recognition in recent 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 embedding 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 information and could be well trained for node classification under the DNN framework. Extensive experiments 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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2019.07.006