Line graph

Null graph

Voltage graph

Theoretical computer science

Degree (music)

Computer science

Acoustics

Pooling

Physics

Simplex graph

Graph

Artificial intelligence

Artificial neural network

Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn powerful node-level or graph-level representation. However, most of the existing graph neural networks suffer from the following limitations: (1) there is limited analysis regarding the graph convolution properties, such as seed-oriented, degree-aware and order-free; (2) the node's degreespecific graph structure is not explicitly expressed in graph convolution for distinguishing structure-aware node neighborhoods; (3) the theoretical explanation regarding the graph-level pooling schemes is unclear.

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification