LINE: Large-scale Information Network Embedding

12 Mar 2015Jian Tang • Meng Qu • Mingzhe Wang • Ming Zhang • Jun Yan • Qiaozhu Mei

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted.

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Task Dataset Model Metric name Metric value Global rank Compare
Node Classification BlogCatalog LINE Accuracy 20.50% # 5
Node Classification BlogCatalog LINE Macro-F1 0.192 # 5
Node Classification Wikipedia LINE Accuracy 17.50% # 5
Node Classification Wikipedia LINE Macro-F1 0.164 # 5