Batch Mode Active Learning for Node Classification in Assortative and Disassortative Networks
Active learning for networked data that focuses on predicting the labels of other nodes accurately by knowing the labels of a small subset of nodes is attracting more and more researchers because it is very useful especially in cases, where labeled data are expensive to obtain. However, most existin...
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doaj-53645b9eda184a63872677488848ca102021-03-29T20:30:08ZengIEEEIEEE Access2169-35362018-01-0164750475810.1109/ACCESS.2017.27798108141855Batch Mode Active Learning for Node Classification in Assortative and Disassortative NetworksShuqiu Ping0https://orcid.org/0000-0001-8881-038XDayou Liu1Bo Yang2Yungang Zhu3https://orcid.org/0000-0002-8305-4092Hechang Chen4Zheng Wang5Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaActive learning for networked data that focuses on predicting the labels of other nodes accurately by knowing the labels of a small subset of nodes is attracting more and more researchers because it is very useful especially in cases, where labeled data are expensive to obtain. However, most existing research either only apply to networks with assortative community structure or focus on node attribute data with links or are designed for working in single mode that will work at a higher learning and query cost than batch active learning in general. In view of this, in this paper, we propose a batch mode active learning method which uses information-theoretic techniques and random walk to select which nodes to label. The proposed method requires only network topology as its input, does not need to know the number of blocks in advance, and makes no initial assumptions about how the blocks connect. We test our method on two different types of networks: assortative structure and diassortative structure, and then compare our method with a single mode active learning method that is similar to our method except for working in single mode and several simple batch mode active learning methods using information-theoretic techniques and simple heuristics, such as employing degree or betweenness centrality. The experimental results show that the proposed method in this paper significantly outperforms them.https://ieeexplore.ieee.org/document/8141855/Machine learningcomplex networksdata mining |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shuqiu Ping Dayou Liu Bo Yang Yungang Zhu Hechang Chen Zheng Wang |
spellingShingle |
Shuqiu Ping Dayou Liu Bo Yang Yungang Zhu Hechang Chen Zheng Wang Batch Mode Active Learning for Node Classification in Assortative and Disassortative Networks IEEE Access Machine learning complex networks data mining |
author_facet |
Shuqiu Ping Dayou Liu Bo Yang Yungang Zhu Hechang Chen Zheng Wang |
author_sort |
Shuqiu Ping |
title |
Batch Mode Active Learning for Node Classification in Assortative and Disassortative Networks |
title_short |
Batch Mode Active Learning for Node Classification in Assortative and Disassortative Networks |
title_full |
Batch Mode Active Learning for Node Classification in Assortative and Disassortative Networks |
title_fullStr |
Batch Mode Active Learning for Node Classification in Assortative and Disassortative Networks |
title_full_unstemmed |
Batch Mode Active Learning for Node Classification in Assortative and Disassortative Networks |
title_sort |
batch mode active learning for node classification in assortative and disassortative networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
Active learning for networked data that focuses on predicting the labels of other nodes accurately by knowing the labels of a small subset of nodes is attracting more and more researchers because it is very useful especially in cases, where labeled data are expensive to obtain. However, most existing research either only apply to networks with assortative community structure or focus on node attribute data with links or are designed for working in single mode that will work at a higher learning and query cost than batch active learning in general. In view of this, in this paper, we propose a batch mode active learning method which uses information-theoretic techniques and random walk to select which nodes to label. The proposed method requires only network topology as its input, does not need to know the number of blocks in advance, and makes no initial assumptions about how the blocks connect. We test our method on two different types of networks: assortative structure and diassortative structure, and then compare our method with a single mode active learning method that is similar to our method except for working in single mode and several simple batch mode active learning methods using information-theoretic techniques and simple heuristics, such as employing degree or betweenness centrality. The experimental results show that the proposed method in this paper significantly outperforms them. |
topic |
Machine learning complex networks data mining |
url |
https://ieeexplore.ieee.org/document/8141855/ |
work_keys_str_mv |
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