TLINE: Scalable Transductive Network Embedding
Network embedding is a classical task which aims to project a network into a low dimensional space. Currently, most existing embedding methods are unsupervised algorithms, which ignore the useful label information. In this paper, we propose TLINE, a semi supervised extension of LINE algorithm. TLINE is a transductive network embedding method, which optimizes the loss function of LINE to preserve both local and global network structure information, and applies SVM to maximize the margin between the labeled nodes of different classes. By applying the edge sampling and the negative sampling techniques in the optimizing process, the computational complexity of TLINE is reduced. Thus TLINE can handle the large scale network. To evaluate the performance in node classification task, we test our methods on two real world network datasets, which are Citeseer and DBLP. The experimental result indicates that TLINE outperforms state of the art baselines and is suitable for large scale networks.
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