Active discriminative network representation learning. Gao, L., Yang, H., Zhou, C., Wu, J., Pan, S., & Hu, Y. In IJCAI International Joint Conference on Artificial Intelligence, IJCAI, volume 2018-July, pages 2142-2148 (CORE Ranked A*), 7, 2018. International Joint Conferences on Artificial Intelligence Organization. doi abstract bibtex 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.
@inproceedings{
title = {Active discriminative network representation learning},
type = {inproceedings},
year = {2018},
pages = {2142-2148 (CORE Ranked A*)},
volume = {2018-July},
month = {7},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
city = {California},
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created = {2018-07-21T08:24:49.331Z},
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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.},
bibtype = {inproceedings},
author = {Gao, Li and Yang, Hong and Zhou, Chuan and Wu, Jia and Pan, Shirui and Hu, Yue},
doi = {10.24963/ijcai.2018/296},
booktitle = {IJCAI International Joint Conference on Artificial Intelligence, IJCAI}
}
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