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Article

Link-based classification

Authors:

Lise GetoorAuthors Info & Claims

ICML'03: Proceedings of the Twentieth International Conference on International Conference on Machine Learning

Pages 496 - 503

Published: 21 August 2003 Publication History

Abstract

A key challenge for machine learning is tackling the problem of mining richly structured data sets, where the objects are linked in some way due to either an explicit or implicit relationship that exists between the objects. Links among the objects demonstrate certain patterns, which can be helpful for many machine learning tasks and are usually hard to capture with traditional statistical models. Recently there has been a surge of interest in this area, fueled largely by interest in web and hypertext mining, but also by interest in mining social networks, bibliographic citation data, epidemiological data and other domains best described using a linked or graph structure. In this paper we propose a framework for modeling link distributions, a link-based model that supports discriminative models describing both the link distributions and the attributes of linked objects. We use a structured logistic regression model, capturing both content and links. We systematically evaluate several variants of our link-based model on a range of data sets including both web and citation collections. In all cases, the use of the link distribution improves classification accuracy.

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Cited By

Wu SXiong YLiang HWeng C(2025)D2-GCN: a graph convolutional network with dynamic disentanglement for node classificationFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-3339-719:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11704-023-3339-7
Dasaratha SPuranam SPhogat KTiyyagura SDuffy NElkind E(2023)DeepPSLProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/401(3606-3614)Online publication date: 19-Aug-2023
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Wu HNg M(2022)Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node ClassificationACM Transactions on Knowledge Discovery from Data10.1145/349456716:4(1-19)Online publication date: 8-Jan-2022
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Information

Published In

cover image Guide Proceedings

ICML'03: Proceedings of the Twentieth International Conference on International Conference on Machine Learning

August 2003

935 pages

ISBN:1577351894

Editors:
Tom Fawcett
HP Labs
,
Nina Mishra
HP Labs and Stanford University

Sponsors

Kluwer Academic Publishers
NSF: National Science Foundation
Kaidara Software
AAAI: American Association for Artificial Intelligence
Microsoft Research: Microsoft Research
HP: HP

Publisher

AAAI Press

Publication History

Published: 21 August 2003

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Cited By

Wu SXiong YLiang HWeng C(2025)D2-GCN: a graph convolutional network with dynamic disentanglement for node classificationFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-3339-719:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11704-023-3339-7
Dasaratha SPuranam SPhogat KTiyyagura SDuffy NElkind E(2023)DeepPSLProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/401(3606-3614)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/401
Wu HNg M(2022)Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node ClassificationACM Transactions on Knowledge Discovery from Data10.1145/349456716:4(1-19)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3494567
Xu DCheng WLuo DChen HZhang XRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)InfoGCLProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3542588(30414-30425)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3542588
Jiang ZGao ZLan JYang HLu YLiu X(2020)Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph EmbeddingProceedings of The Web Conference 202010.1145/3366423.3380230(1581-1591)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380230
Goyal PRaja SHuang DChhetri SCanedo AMondal AShree JJawahar CAlhajj R(2020)Graph representation ensemble learningProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381465(24-31)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/ASONAM49781.2020.9381465
Mercado PTudisco FHein MWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Generalized matrix means for semi-supervised learning with multilayer graphsProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3455619(14877-14886)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3455619
Hajiramezanali EHasanzadeh ADuffield NNarayanan KZhou MQian XWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Semi-implicit graph variational auto-encodersProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3455248(10712-10723)Online publication date: 8-Dec-2019
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Yang LWu FWang YGu JGuo Y(2019)Masked graph convolutional networkProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367607(4070-4077)Online publication date: 10-Aug-2019
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Yang LChen ZGu JGuo Y(2019)Dual self-paced graph convolutional networkProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367606(4062-4069)Online publication date: 10-Aug-2019
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