Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77304
Title: Deep network embedding with aggregated proximity preserving
Authors: Shen, X 
Chung, FL 
Keywords: Graph representation
Network embedding
Network proximity
Semi-supervised
Stacked auto-encoder
Issue Date: 2017
Publisher: Association for Computing Machinery, Inc
Source: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, 31 Jul -3 Aug 2017, p. 40-43 How to cite?
Abstract: Network embedding is an effective method to learn a low-dimensional feature vector representation for each node of a given network. In this paper, we propose a deep network embedding model with aggregated proximity preserving (DNE-APP). Firstly, an overall network proximity matrix is generated to capture both local and global network structural information, by aggregating different k-th order network proximities between different nodes. Then, a semi-supervised stacked auto-encoder is employed to learn the hidden representations which can best preserve the aggregated proximity in the original network, and also map the node pairs with higher proximity closer to each other in the embedding space. With the hidden representations learned by DNE-APP, we apply vector-based machine learning techniques to conduct node classification and link label prediction tasks on the real-world datasets. Experimental results demonstrate the superiority of our proposed DNE-APP model over the state-of-the-art network embedding algorithms.
URI: http://hdl.handle.net/10397/77304
ISBN: 9781450349932
DOI: 10.1145/3110025.3110035
Appears in Collections:Conference Paper

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