Graph Convolutional Networks with EigenPooling

30 Apr 2019 Yao Ma Suhang Wang Charu C. Aggarwal Jiliang Tang

Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have been proven to improve the performance of many graph related tasks such as node classification and link prediction... (read more)

PDF Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Graph Classification D&D EigenGCN-3 Accuracy 78.6% # 20
Graph Classification ENZYMES EigenGCN-3 Accuracy 65.0% # 10
Graph Classification MUTAG EigenGCN-3 Accuracy 79.5% # 53
Graph Classification NC1 EigenGCN-3 Accuracy 0.770 # 1
Graph Classification NCI109 EigenGCN-3 Accuracy 74.90 # 11
Graph Classification PROTEINS EigenGCN-3 Accuracy 76.60% # 23

Methods used in the Paper


METHOD TYPE
GCN
Graph Models