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On Transductive Classification in Heterogeneous Information Networks

Published: 24 October 2016 Publication History

Abstract

A heterogeneous information network (HIN) is used to model objects of different types and their relationships. Objects are often associated with properties such as labels. In many applications, such as curated knowledge bases for which object labels are manually given, only a small fraction of the objects are labeled. Studies have shown that transductive classification is an effective way to classify and to deduce labels of objects, and a number of transductive classifiers have been put forward to classify objects in an HIN. We study the performance of a few representative transductive classification algorithms on HINs. We identify two fundamental properties, namely, cohesiveness and connectedness, of an HIN that greatly influence the effectiveness of transductive classifiers. We define metrics that measure the two properties. Through experiments, we show that the two properties serve as very effective indicators that predict the accuracy of transductive classifiers. Based on cohesiveness and connectedness we derive (1) a black-box tester that evaluates whether transductive classifiers should be applied for a given classification task and (2) an active learning algorithm that identifies the objects in an HIN whose labels should be sought in order to improve classification accuracy.

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  • (2022)SCHAIN-IRAM: An Efficient and Effective Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299793834:4(1980-1992)Online publication date: 1-Apr-2022
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  • (2021)Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00084(912-923)Online publication date: Apr-2021
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    cover image ACM Conferences
    CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
    October 2016
    2566 pages
    ISBN:9781450340731
    DOI:10.1145/2983323
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    Publication History

    Published: 24 October 2016

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    Author Tags

    1. heterogeneous information network
    2. knowledge base
    3. transductive classification

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    • Hong Kong Research Grants Council GRF grant

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    CIKM'16: ACM Conference on Information and Knowledge Management
    October 24 - 28, 2016
    Indiana, Indianapolis, USA

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    CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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    View all
    • (2022)SCHAIN-IRAM: An Efficient and Effective Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299793834:4(1980-1992)Online publication date: 1-Apr-2022
    • (2021)DFraud³: Multi-Component Fraud Detection Free of Cold-StartIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.308125816(3456-3468)Online publication date: 2021
    • (2021)Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00084(912-923)Online publication date: Apr-2021
    • (2019)Neural Embedding Propagation on Heterogeneous Networks2019 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2019.00080(698-707)Online publication date: Nov-2019
    • (2019)A Random Walk Tensor Model for Heterogeneous Network Entity ClassificationIEEE Access10.1109/ACCESS.2019.29182407(72749-72760)Online publication date: 2019
    • (2018)Automatic opioid user detection from TwitterProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304234(3357-3363)Online publication date: 13-Jul-2018
    • (2018)Heterogeneous Neural Attentive Factorization Machine for Rating PredictionProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271759(833-842)Online publication date: 17-Oct-2018
    • (2018)Collective Classification of Spam Campaigners on TwitterProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186119(529-538)Online publication date: 10-Apr-2018
    • (2018)Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights2018 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2018.00081(657-666)Online publication date: Nov-2018
    • (2017)Semi-supervised learning over heterogeneous information networks by ensemble of meta-graph guided random walksProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172158(1944-1950)Online publication date: 19-Aug-2017
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