Representation Learning on Graphs with Jumping Knowledge Networks

Understand

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.