关键词: 直推式, 网络表示学习, 结点分类

Abstract: Network embedding is a classical task which aims to project a network into a low-dimensional space. Currently, most of existing embedding methods are unsupervised algorithms, which ignore useful label information. This paper proposes TLINE, a semi-supervised extension of LINE (large-scale information network embedding) algorithm. TLINE is a transductive network embedding method, which optimizes the loss function of LINE to preserve local network structure information, and applies SVM (support vector machine) to max the margin between the labeled nodes of different classes. By applying edge-sampling, negative sampling techniques and asynchronous stochastic gradient descent algorithm 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, this paper tests the proposed methods on two real world network datasets, CiteSeer and DBLP. The experimental results indicate that TLINE outperforms the state-of-the-art baselines and is suitable for large-scale network.

Key words: transductive, network embedding learning, node classification