Paper Info
Title
Noise-Aware Network Embedding for Multiplex Network
Abstract
Network embedding aims at learning the latent representations of nodes while preserving the complex structure of the underlying graph. Real-world networks are usually related with each other via common nodes, the so-called multiplex network. To make the data mining work on the multiplex network more actionable, it become urgent and essential to transform it into low-dimension vector space. Recently, several works have been proposed to leverage the complementary information for embedding. However, they suffer from sacrificing distinct properties of the counterparts in different layers, as they preserve much noise information into embedding vectors. In this paper, we propose a Noise-Aware Network Embedding approach for Multiplex Network, namely NANE. Unlike previous works, NANE considers the roles of an identical node in different layers, and adopts a more robust and flexible strategy to rationally integrate the cross-layer information while keeping the unique characteristic of each layer. We perform extensive evaluations on several real-world datasets. The experimental results demonstrate that our NANE can achieve better performance on link prediction task and significantly outperform previous methods especially in noisy multiplex network scenarios.
Year
DOI
Venue
2019
10.1109/IJCNN.2019.8851949
2019 International Joint Conference on Neural Networks (IJCNN)
Keywords
Field
DocType
real-world networks,embedding vectors,noisy multiplex network scenarios,noise-aware network embedding approach,common nodes,data mining,low-dimension vector space,NANE,link prediction task
Data mining,Graph,Vector space,Embedding,Computer science,Multiplex,Artificial intelligence,Network embedding,Machine learning
Conference
ISSN
ISBN
Citations 
2161-4393
978-1-7281-1986-1
0
PageRank 
References 
Authors
0.34
20
6
Name
Order
Citations
PageRank
Xiaokai Chu162.15
Xinxin Fan2165.10
Di Yao3417.40
Chen-Lin Zhang4665.57
Jianhui Huang5795.71
Jingping Bi6353.44