  Routing methods in capsule networks often learn a hierarchical relationship
for capsules in successive layers, but the intra-relation between capsules in
the same layer is less studied, while this intra-relation is a key factor for
the semantic understanding in text data. Therefore, in this paper, we introduce
a new capsule network with graph routing to learn both relationships, where
capsules in each layer are treated as the nodes of a graph. We investigate
strategies to yield adjacency and degree matrix with three different distances
from a layer of capsules, and propose the graph routing mechanism between those
capsules. We validate our approach on five text classification datasets, and
our findings suggest that the approach combining bottom-up routing and top-down
attention performs the best. Such an approach demonstrates generalization
capability across datasets. Compared to the state-of-the-art routing methods,
the improvements in accuracy in the five datasets we used were 0.82, 0.39,
0.07, 1.01, and 0.02, respectively.
