Negative Link Prediction in Social Media

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework

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

Transfer AdaBoost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features

AbstractIn signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer AdaBoost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time