LINE: Large-scale Information Network Embedding

12 Mar 2015Jian TangMeng QuMingzhe WangMing ZhangJun YanQiaozhu 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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Node Classification BlogCatalog LINE Accuracy 20.50% # 6
Macro-F1 0.192 # 6
Node Classification Wikipedia LINE Accuracy 17.50% # 5
Macro-F1 0.164 # 5