Noise Reduction in Network Embedding

Document Type

Conference Proceeding

Source of Publication

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

Publication Date

1-1-2019

Abstract

© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Network Embedding aims to learn latent representations and effectively preserves structure of network and information of vertices. Recently, networks with rich side information such as vertex’s label and links between vertices have attracted significant interest due to its wide applications such as node classification and link prediction. It’s well known that, in real world applications, network always contains mislabeled vertices and edges, which will cause the embedding preserves mistake information. However, current semi-supervised graph embedding algorithms assume the vertex label is ground-truth. Manually relabel all mislabeled vertices is always inapplicable, therefore, how to effective reduce noise so as to maximize the graph analysis task performance is extremely important. In this paper, we focus on reducing label noise ratio in dataset to obtain more reasonable embedding. We proposed two methods for any semi-supervised network embedding algorithm to tackle it: first approach uses a model to identify potential noise vertices and correct them, second approach uses two voting strategy to precisely relabel vertex. To the best of our knowledge, we are the first to tackle this issue in network embedding. Our experiments are conducted on three public data sets.

ISBN

9783030323875

ISSN

1867-8211

Publisher

Springer

Volume

294 LNCIST

First Page

109

Last Page

120

Disciplines

Computer Sciences

Keywords

Network embedding, Noise identification, Voting

Scopus ID

85076165490

Indexed in Scopus

yes

Open Access

no

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