An Active Noise Correction Graph Embedding Method Based on Active Learning for Graph Noisy Data

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. In various scenarios of the real world, there are various graph data. Most graph structures are confronted with the problems of complex structure and large consumption of memory space. Graph embedding is an effective method to overcome such challenges, which converts graph structure into a low-dimensional dense vector space. In the real world, label acquisition is expensive, and there may be noise in the data. Therefore, it is important to find valuable noise nodes as much as possible to improve the performance of downstream task. In this paper, we propose a novel active sampling strategy for graph noisy data named Active Noise Correction Graph Embedding method (ANCGE). Given the label budget, the proposed method aims to use semi-supervised graph embedding algorithm to find valuable mislabeled nodes. ANCGE measures the value of noise nodes according to their representativeness and influence on the graph. The experimental results on three open datasets demonstrate the effectiveness of our method and its stability under different noise rates.

ISBN

9783030323875

ISSN

1867-8211

Publisher

Springer

Volume

294 LNCIST

First Page

419

Last Page

433

Disciplines

Computer Sciences

Keywords

Active correction, Active learning, Graph embedding, Noisy label

Scopus ID

85076178969

Indexed in Scopus

yes

Open Access

no

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