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.
|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|