Active discriminative network representation learning

Li Gao, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, Yue Hu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.

Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
Place of PublicationCalifornia USA
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2142-2148
Number of pages7
ISBN (Electronic)9780999241127
Publication statusPublished - 2018
Externally publishedYes
EventInternational Joint Conference on Artificial Intelligence 2018 - Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018
https://www.ijcai.org/proceedings/2018/

Conference

ConferenceInternational Joint Conference on Artificial Intelligence 2018
Abbreviated titleIJCAI 2018
CountrySweden
CityStockholm
Period13/07/1819/07/18
Internet address

Cite this

Gao, L., Yang, H., Zhou, C., Wu, J., Pan, S., & Hu, Y. (2018). Active discriminative network representation learning. In J. Lang (Ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 (pp. 2142-2148). California USA: International Joint Conferences on Artificial Intelligence.
Gao, Li ; Yang, Hong ; Zhou, Chuan ; Wu, Jia ; Pan, Shirui ; Hu, Yue. / Active discriminative network representation learning. Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. editor / Jerome Lang. California USA : International Joint Conferences on Artificial Intelligence, 2018. pp. 2142-2148
@inproceedings{0c24935abf244665920c041994fb3124,
title = "Active discriminative network representation learning",
abstract = "Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.",
author = "Li Gao and Hong Yang and Chuan Zhou and Jia Wu and Shirui Pan and Yue Hu",
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Gao, L, Yang, H, Zhou, C, Wu, J, Pan, S & Hu, Y 2018, Active discriminative network representation learning. in J Lang (ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. International Joint Conferences on Artificial Intelligence, California USA, pp. 2142-2148, International Joint Conference on Artificial Intelligence 2018, Stockholm, Sweden, 13/07/18.

Active discriminative network representation learning. / Gao, Li; Yang, Hong; Zhou, Chuan; Wu, Jia; Pan, Shirui; Hu, Yue.

Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. ed. / Jerome Lang. California USA : International Joint Conferences on Artificial Intelligence, 2018. p. 2142-2148.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AU - Wu, Jia

AU - Pan, Shirui

AU - Hu, Yue

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N2 - Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.

AB - Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.

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Gao L, Yang H, Zhou C, Wu J, Pan S, Hu Y. Active discriminative network representation learning. In Lang J, editor, Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. California USA: International Joint Conferences on Artificial Intelligence. 2018. p. 2142-2148