  Graph Convolutional Networks (GCNs) have gained significant developments in
representation learning on graphs. However, current GCNs suffer from two common
challenges: 1) GCNs are only effective with shallow structures; stacking
multiple GCN layers will lead to over-smoothing. 2) GCNs do not scale well with
large, dense graphs due to the recursive neighborhood expansion. We generalize
the propagation strategies of current GCNs as a \emph{"Sink$\to$Source"} mode,
which seems to be an underlying cause of the two challenges. To address these
issues intrinsically, in this paper, we study the information propagation
mechanism in a \emph{"Source$\to$Sink"} mode. We introduce a new concept
"information flow path" that explicitly defines where information originates
and how it diffuses. Then a novel framework, namely Flow Graph Network
(FlowGN), is proposed to learn node representations. FlowGN is computationally
efficient and flexible in propagation strategies. Moreover, FlowGN decouples
the layer structure from the information propagation process, removing the
interior constraint of applying deep structures in traditional GCNs. Further
experiments on public datasets demonstrate the superiority of FlowGN against
state-of-the-art GCNs.
