  We propose a new network architecture, Gated Attention Networks (GaAN), for
learning on graphs. Unlike the traditional multi-head attention mechanism,
which equally consumes all attention heads, GaAN uses a convolutional
sub-network to control each attention head's importance. We demonstrate the
effectiveness of GaAN on the inductive node classification problem. Moreover,
with GaAN as a building block, we construct the Graph Gated Recurrent Unit
(GGRU) to address the traffic speed forecasting problem. Extensive experiments
on three real-world datasets show that our GaAN framework achieves
state-of-the-art results on both tasks.
