Unsupervised link prediction using aggregative statistics on heterogeneous social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic graphical model for brand reputation assessment in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Latent feature learning in social media network
Proceedings of the 21st ACM international conference on Multimedia
Inferring anchor links across multiple heterogeneous social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Transferring heterogeneous links across location-based social networks
Proceedings of the 7th ACM international conference on Web search and data mining
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Link prediction and recommendation is a fundamental problem in social network analysis. The key challenge of link prediction comes from the sparsity of networks due to the strong disproportion of links that they have potential to form to links that do form. Most previous work tries to solve the problem in single network, few research focus on capturing the general principles of link formation across heterogeneous networks. In this work, we give a formal definition of link recommendation across heterogeneous networks. Then we propose a ranking factor graph model (RFG) for predicting links in social networks, which effectively improves the predictive performance. Motivated by the intuition that people make friends in different networks with similar principles, we find several social patterns that are general across heterogeneous networks. With the general social patterns, we develop a transfer-based RFG model that combines them with network structure information. This model provides us insight into fundamental principles that drive the link formation and network evolution. Finally, we verify the predictive performance of the presented transfer model on 12 pairs of transfer cases. Our experimental results demonstrate that the transfer of general social patterns indeed help the prediction of links.