According to model structure and training strategies, deep learning algorithms on graphs can be divided into 5 categories:

1)	Graph Recurrent Neural Networks

2)	Graph Convolutional Networks

3)	Graph Autoencoders

4)	Graph Reinforcement Learning

5)	Graph Adversarial Methods


Types of Deep Learning Algorithms on Graphs

1)	Node-focused tasks: these tasks predict value or class for each single node or edge in the testing set. Examples include node classification, link prediction, node recommendation.

2)	Graph-focused tasks: these tasks predict value or class for the entire graph, and testing set contains multiple graphs. Examples include graph classification and graph generation. 


Types of Learning Tasks for Deep Learning on Graphs

In deep learning, a graph is represented as $G=(V,E)$, where $V=\{v_1,v_2,..,v_N\}$  is the set of nodes, and 	$E\subseteq V\times V$ is the set of edges between nodes. $N=|V|$ and $M=|E|$. $\mathbf{A}$ is the adjacency matrix of this graph.

University of Notre Dame

GNNs that build word/entity graphs for reasoning can be used


Graph neural networks for NRE


Zhang, Z., Cui, P., & Zhu, W. (2020). Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering.

Learn Before

Related

Learn After