  Recent deep learning approaches for representation learning on graphs follow
a neighborhood aggregation procedure. We analyze some important properties of
these models, and propose a strategy to overcome those. In particular, the
range of "neighboring" nodes that a node's representation draws from strongly
depends on the graph structure, analogous to the spread of a random walk. To
adapt to local neighborhood properties and tasks, we explore an architecture --
jumping knowledge (JK) networks -- that flexibly leverages, for each node,
different neighborhood ranges to enable better structure-aware representation.
In a number of experiments on social, bioinformatics and citation networks, we
demonstrate that our model achieves state-of-the-art performance. Furthermore,
combining the JK framework with models like Graph Convolutional Networks,
GraphSAGE and Graph Attention Networks consistently improves those models'
performance.
