GMNN: Graph Markov Neural Networks

15 May 2019Meng QuYoshua BengioJian Tang

This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks)... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Object Classification Citeseer GMNN Accuracy 72.9 # 1
Object Classification Cora GMNN Accuracy 83.7 # 1
Object Classification Pubmed GMNN Accuracy 81.8 # 1

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet