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Algorithms 2019, 12(1), 12; https://doi.org/10.3390/a12010012

Edge-Nodes Representation Neural Machine for Link Prediction

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1,2,3,* , 1,2
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1
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China
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School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 29 October 2018 / Revised: 18 December 2018 / Accepted: 28 December 2018 / Published: 2 January 2019
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Abstract

Link prediction is a task predicting whether there is a link between two nodes in a network. Traditional link prediction methods that assume handcrafted features (such as common neighbors) as the link’s formation mechanism are not universal. Other popular methods tend to learn the link’s representation, but they cannot represent the link fully. In this paper, we propose Edge-Nodes Representation Neural Machine (ENRNM), a novel method which can learn abundant topological features from the network as the link’s representation to promote the formation of the link. The ENRNM learns the link’s formation mechanism by combining the representation of edge and the representations of nodes on the two sides of the edge as link’s full representation. To predict the link’s existence, we train a fully connected neural network which can learn meaningful and abundant patterns. We prove that the features of edge and two nodes have the same importance in link’s formation. Comprehensive experiments are conducted on eight networks, experiment results demonstrate that the method ENRNM not only exceeds plenty of state-of-the-art link prediction methods but also performs very well on diverse networks with different structures and characteristics. View Full-Text
Keywords: link prediction; full representation; formation mechanism; neural network link prediction; full representation; formation mechanism; neural network
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Xu, G.; Wang, X.; Wang, Y.; Lin, D.; Sun, X.; Fu, K. Edge-Nodes Representation Neural Machine for Link Prediction. Algorithms 2019, 12, 12.

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