Learning Topological Representation for Networks via Hierarchical Sampling

15 Feb 2019 Guoji Fu Chengbin Hou Xin Yao

The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantages in analyzing large-scale networks... (read more)

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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Link Prediction DBLP HSRL (DW) AUC 84.7 # 3
Link Prediction Douban HSRL (DW) AUC 84.2 # 1
Link Prediction MIT HSRL (DW) AUC 92.6 # 1
Link Prediction Yelp HSRL (DW) AUC 90.1 # 1

Methods used in the Paper


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