NDLI: Semi-supervised Collective Classification in Multi-attribute Network Data

Content Provider	Springer Nature Link
Author	Wang, Shaokai Ye, Yunming Li, Xutao Huang, Xiaohui Lau, Raymond Y. K.
Copyright Year	2016
Abstract	Multi-attribute network refers to network data with multiple attribute views and relational view. Although semi-supervised collective classification has been investigated extensively, little attention is received for such kind of network data. In this paper, we aim to study and solve the semi-supervised learning problem for multi-attribute networks. There are two important challenges: (1) how to extract effective information from the rich multi-attribute and relational information; (2) how to make use of unlabeled data in the network. We propose a new generative model with network regularization, called MARL, which addresses the two challenges. In the approach, a generative model based on the probabilistic latent semantic analysis method is developed to leverage attribute information, and a network regularizer is incorporated to smooth label probability with relational information and unlabeled data. Comprehensive experiments on various data sets have been conducted to demonstrate the effectiveness of the proposed MARL, and the results reveal that our approach outperforms existing collective classification methods and multi-view classification methods in terms of accuracy.
Starting Page	153
Ending Page	172
Page Count	20
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	45
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2016-04-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semi-supervised learning Collective classification Multiple attributes Network data Artificial Intelligence (incl. Robotics) Complex Systems Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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