  Graph neural networks (GNNs) have been intensively applied to various
graph-based applications. Despite their success, manually designing the
well-behaved GNNs requires immense human expertise. And thus it is inefficient
to discover the potentially optimal data-specific GNN architecture. This paper
proposes DFG-NAS, a new neural architecture search (NAS) method that enables
the automatic search of very deep and flexible GNN architectures. Unlike most
existing methods that focus on micro-architectures, DFG-NAS highlights another
level of design: the search for macro-architectures on how atomic propagation
(\textbf{\texttt{P}}) and transformation (\textbf{\texttt{T}}) operations are
integrated and organized into a GNN. To this end, DFG-NAS proposes a novel
search space for \textbf{\texttt{P-T}} permutations and combinations based on
message-passing dis-aggregation, defines four custom-designed
macro-architecture mutations, and employs the evolutionary algorithm to conduct
an efficient and effective search. Empirical studies on four node
classification tasks demonstrate that DFG-NAS outperforms state-of-the-art
manual designs and NAS methods of GNNs.
