This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.
Collective classification has recently attracted significant attention for classifying relational data in the data mining area @cite_6 @cite_17 . Networked data is one typical type of relational data, in which instances are represented as nodes and the relationships between nodes are represented as edges. Collective classification exploits dependencies between instances, which makes it one of the most favorable classification methods for networked data.
@cite_6 @cite_17
2153959628 2100045227
Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data. Many collective classification (CC) algorithms have been shown to increase accuracy when instances are interrelated. However, CC algorithms must be carefully applied because their use of estimated labels can in some cases decrease accuracy. In this article, we show that managing this label uncertainty through cautious algorithmic behavior is essential to achieving maximal, robust performance. First, we describe cautious inference and explain how four well-known families of CC algorithms can be parameterized to use varying degrees of such caution. Second, we introduce cautious learning and show how it can be used to improve the performance of almost any CC algorithm, with or without cautious inference. We then evaluate cautious inference and learning for the four collective inference families, with three local classifiers and a range of both synthetic and real-world data. We find that cautious learning and cautious inference typically outperform less cautious approaches. In addition, we identify the data characteristics that predict more substantial performance differences. Our results reveal that the degree of caution used usually has a larger impact on performance than the choice of the underlying inference algorithm. Together, these results identify the most appropriate CC algorithms to use for particular task characteristics and explain multiple conflicting findings from prior CC research.