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Title

Conditional Random Field Enhanced Graph Convolutional Neural Network

Year: 1400
COI: ICTBC04_002
Language: EnglishView: 124
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Authors

Samaneh Ghanipour - Master of Software Engineering, Qazvin Azad University

Abstract:

to explore deep neural networks for feature learning on graphs. Different from the regular image and sequence data, graph data encode the complicated relational information between different nodes, which challenges the classical deep neural networks. Moreover, in real-world applications, the label of nodes in graph data is usually not available, which makes the feature learning on graphs more difficult. To address these challenging issues, this Paper is focusing on designing new deep neural networks to effectively explore the relational information for unsupervised feature learning on graph data.First, to address the sparseness issue of the relational information, I propose a new proximity generative adversarial network which can discover the underlying relational information for learning better node representations. Meanwhile, a new self-paced network embedding method is designed to address the unbalance issue of the relational information when learning node representations. Additionally, to deal with rich attributes associated to nodes, I develop a new deep neural network to capture various relational information in both topological structure and node attributes for enhancing network embedding. Furthermore, to preserve the relational information in the hidden layers of deep neural networks, I develop a novel graph convolutional neural network (GCN) based on conditional random fields, which is the first algorithm applying this kind of graphical models to graph neural networks in an unsupervised manner.

Keywords:

Paper COI Code

This Paper COI Code is ICTBC04_002. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/1259786/

How to Cite to This Paper:

If you want to refer to this Paper in your research work, you can simply use the following phrase in the resources section:
Ghanipour, Samaneh,1400,Conditional Random Field Enhanced Graph Convolutional Neural Network,Fourth International Conference on Information Technology, Computer and Telecommunication Engineering of Iran,Tehran,https://civilica.com/doc/1259786

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