Graph Convolutional Networks with EigenPooling

Understand

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.
• To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded.
• A common way is to globally combine the node representations.