GraRep: Learning Graph Representations with Global Structural Information

WWW 2015 Shaosheng CaoWei LuQiongkai Xu

In this paper, we present {GraRep}, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process... (read more)

PDF Abstract

Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Node Classification 20NEWS GraRep Accuracy 81.44 # 1
Node Classification BlogCatalog GraRep Macro-F1 0.3093 # 1