  Brain graph representation learning serves as the fundamental technique for
brain diseases diagnosis. Great efforts from both the academic and industrial
communities have been devoted to brain graph representation learning in recent
years. The isomorphic neural network (IsoNN) introduced recently can
automatically learn the existence of sub-graph patterns in brain graphs, which
is also the state-of-the-art brain graph representation learning method by this
context so far. However, IsoNN fails to capture the orientations of sub-graph
patterns, which may render the learned representations to be useless for many
cases. In this paper, we propose a new Iso-CapsNet (Isomorphic Capsule Net)
model by introducing the graph isomorphic capsules for effective brain graph
representation learning. Based on the capsule dynamic routing, besides the
subgraph pattern existence confidence scores, Iso-CapsNet can also learn other
sub-graph rich properties, including position, size and orientation, for
calculating the class-wise digit capsules. We have compared Iso-CapsNet with
both classic and state-of-the-art brain graph representation approaches with
extensive experiments on four brain graph benchmark datasets. The experimental
results also demonstrate the effectiveness of Iso-CapsNet, which can
out-perform the baseline methods with significant improvements.
