  Graph neural networks (GNNs) emerged recently as a standard toolkit for
learning from data on graphs. Current GNN designing works depend on immense
human expertise to explore different message-passing mechanisms, and require
manual enumeration to determine the proper message-passing depth. Inspired by
the strong searching capability of neural architecture search (NAS) in CNN,
this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed
search space. The GNAS can automatically learn better architecture with the
optimal depth of message passing on the graph. Specifically, we design Graph
Neural Architecture Paradigm (GAP) with tree-topology computation procedure and
two types of fine-grained atomic operations (feature filtering and neighbor
aggregation) from message-passing mechanism to construct powerful graph network
search space. Feature filtering performs adaptive feature selection, and
neighbor aggregation captures structural information and calculates neighbors'
statistics. Experiments show that our GNAS can search for better GNNs with
multiple message-passing mechanisms and optimal message-passing depth. The
searched network achieves remarkable improvement over state-of-the-art manual
designed and search-based GNNs on five large-scale datasets at three classical
graph tasks. Codes can be found at https://github.com/phython96/GNAS-MP.
