CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Seamlessly Integrating Effective Links with Attributes for Networked Data Classification
Zhao,Yangyang; Sun,Zhengya; Xu,Changsheng; Hao,Hongwei
2015-05
Conference NameThe 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
Source PublicationAdvances in Knowledge Discovery and Data Mining
Conference DateMay 19, 2015 - May 22, 2015
Conference PlaceHo Chi Minh City, Vietnam
Abstract
Networked data is emerging with great amount in various fields like social networks, biological networks, research publication networks, etc. Networked data classification is therefore of critical importance in real world, and it is noticed that link information can help
improve learning performance. However, classification of such networked data can be challenging since: 1) the original links (also referred as relations) in such networks, are always sparse, incomplete and noisy; 2) it is not easy to characterize, select and leverage effective link information from the networks, involving multiple types of links with distinct
semantics; 3) it is difficult to seamlessly integrate link information with attribute information in a network. To address these limitations, in this paper we develop a novel Seamlessly-integrated Link-Attribute Collective Matrix Factorization (SLA-CMF) framework, which mines highly effective link information given arbitrary information network and leverages
it with attribute information in a unified perspective. Algorithmwise, SLA-CMF first mines highly effective link information via link path weighting and link strength learning. Then it learns a low-dimension linkattribute joint representation via graph Laplacian CMF. Finally the joint representation is put into a traditional classifier such as SVM for classification.
Extensive experiments on benchmark datasets demonstrate the effectiveness of our method.
KeywordNetworked Data Classification Heterogeneous Information Fusion Collective Matrix Factorization
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11951
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorSun,Zhengya
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhao,Yangyang,Sun,Zhengya,Xu,Changsheng,et al. Seamlessly Integrating Effective Links with Attributes for Networked Data Classification[C],2015.
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