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Application of Bayesian Belief Networks for Smart City Fire Risk Assessment Using History Statistics and Sensor Data Read chapter Read first chapter

2020 | OriginalPaper | Chapter

A New Model for Predicting Node Type Based on Deep Learning

Authors : Bo Gong, Daji Ergu, Kuiyi Liu, Ying Cai

Published in: Data Science

Publisher: Springer Singapore

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Abstract

With the development of the Internet, a large number of data sets are generated, which contain valuable resources. Meanwhile, there are various graphical representations in real life, such as social networks, citation networks, and user networks. For user networks, there also exists rich information about entities except the network structure. Therefore, predicting the type of nodes in the network can help us quickly identify user type, citations type etc. In this paper, a new method based on deep learning is proposed to predict the class of node. Two public data sets are used as training sets. First, the node features are embedded to pre-train the neighbor’s neighborhood structure features, then the pre-trained data is used to input to the classification model, and the structural feature parameters are loaded. The final result shows that the prediction accuracy is increased by nearly 25% higher than the baseline model. The F1 scores of the model tested on the two data sets are 83.5% and 80.2%, respectively.

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Metadata
Title
A New Model for Predicting Node Type Based on Deep Learning
Authors
Bo Gong
Daji Ergu
Kuiyi Liu
Ying Cai
Copyright Year
2020
Publisher
Springer Singapore
Book
Data Science

Print ISBN: 978-981-15-2809-5

Electronic ISBN: 978-981-15-2810-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-2810-1_20

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