This thesis explores deep learning on graphs, focusing on encoding and decoding.
problem Deep learning on graph-structured data is underexplored.
method Introduces Edge-Conditioned Convolutions (ECC) for graph encoding and SuperPoint Graph for intermediate representation.
result Developed GraphVAE for generating graphs with variable node counts.