An on-line sequential learning method in social networks for node classification
An on-line sequential learning method in social networks for node classification
复制标题
社交网络中节点分类的在线顺序学习方法
DOI：
10.1016/j.neucom.2014.04.074
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发表时间：
2015-02
期刊：
影响因子：
6
通讯作者：
Guoren Wang
中科院分区：
文献类型：
--
作者：
Yongjiao Sun;Ye Yuan;Guoren Wang
Social networks have been a common platform for human interactions due to the rapid development of Internet. Along with the rising demand in network analysis, the issue of node classification have became an important research field. This article aims to address the task of node classification using an on-line sequential learning method with the role of links and node features. Compared to the conventional classification methods, we should not only use the node features for node classification, but also consider the interaction among the linked nodes. In this paper, we assume that the nodes have been partially labeled in a social network, and we use these labeled nodes to predict the categories of unlabeled nodes. Based on OS-ELM learning method, three node classification approaches are proposed. First, considering the influence of other nodes, we combine the linkage information with the features of the trained nodes to learn the classifier. Then, for reducing the learning time, we present a method to refine the node features. Finally, according to graph structures, we present an optimization node classification method. Extensive experiments were conducted to verify the performance of our proposed methods.
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DOI：
10.1007/978-3-642-24425-4_40
发表时间：
2011-10
期刊：
--
影响因子：
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作者：
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通讯作者：
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DOI：
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期刊：
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DOI：
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发表时间：
2008-09
期刊：
--
影响因子：
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作者：
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通讯作者：
Prithviraj Sen;Galileo Namata;M. Bilgic;L. Getoor;Brian Gallagher;Tina Eliassi-Rad
影响因子：
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作者：
Liang, Nan-Ying;Huang, Guang-Bin;Sundararajan, N.
通讯作者：
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DOI：
10.1073/pnas.0908366106
发表时间：
2009-12-29
影响因子：
11.1
作者：
Guimera, Roger;Sales-Pardo, Marta
通讯作者：
Sales-Pardo, Marta