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Since both <math>\tilde{A}</math> and <math>\tilde{D}</math> are able to be pre-calculated, this graph convolution method can be easily accelerated by GPU implementation. '''Note that this method only suits for undirected graphs with no edge features.'''
==== Spatial approaches ====
Spatial approaches directly design convolution operation on the graph based on the graph topology (hence called '''spatial'''), making these methods more flexible compared with spectral approaches. Since the size of neighbors is mostly different for each node within a graph, designing an efficient way to define receptive fields and feature propagation is the prime challenge of such approaches.
===== GAT<ref>{{Cite journal|last=Veličković|first=Petar|last2=Cucurull|first2=Guillem|last3=Casanova|first3=Arantxa|last4=Romero|first4=Adriana|last5=Liò|first5=Pietro|last6=Bengio|first6=Yoshua|date=2018-02-04|title=Graph Attention Networks|url=http://arxiv.org/abs/1710.10903|journal=International Conference on Learning Representations (ICLR), 2018}}</ref> =====
== References ==
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