Transfer Learning across Networks for Collective Classification

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.Comment: Published in the proceedings of IEEE ICDM 201

Hospital Treatment Rates and Spill-Over Effects: Does Ownership Matter?

This paper studies the effect of hospital ownership on treatment rates allowing for spatial correlation among hospitals. Competition among hospitals and knowledge spillovers generate significant externalities which we try to capture using the spatial Durbin model. Using a panel of 2342 hospitals in the 48 continental states observed over the period 2005 to 2008, we find significant spatial correlation of medical service treatment rates among hospitals. The paper also shows mixed results on the effect of hospital ownership on treatment rates that depends upon the market structure where the hospital is located and which varies by treatment type

Assessing the Effects of Privatisation, Competition and Regulation on Economic Performance: The Case of Electricity Sector Reform

Over the last two decades electricity sectors in both developed and developing countries have been subject to restructuring to introduce private capital and increase competition. This has been accompanied by the introduction of new regulatory regimes. Although the effects of such reforms in a number of the developed economies are now well documented, apart from a few case studies the experience of developing countries is much less well researched. This is important because privatisation, competition and the reform of state regulation are key themes of donor aid programmes, notably those of the World Bank. This paper provides an econometric assessment of the effects of privatisation, competition and regulation on the performance of the electricity generation industry using panel data for 36 developing and transitional countries, over the period 1985 to 2003. The study identifies the impact of these reforms on generating capacity, electricity generated, labour productivity in the generating sector and capacity utilisation. The main conclusions are that on their own privatisation and regulation do not lead to obvious gains in economic performance, though there are some positive interaction effects. By contrast, introducing competition does seem to be effective in stimulating performance improvements.Privatisation, competition, regulation, developing economies, electricity sector.