Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Graph Capsule Network   Graph Neural Networks (GNNs) draw their strength from explicitly modeling the
topological information of structured data. However, existing GNNs suffer from
limited capability in capturing the hierarchical graph representation which
plays an important role in graph classification. In this paper, we innovatively
propose hierarchical graph capsule network (HGCN) that can jointly learn node
embeddings and extract graph hierarchies. Specifically, disentangled graph
capsules are established by identifying heterogeneous factors underlying each
node, such that their instantiation parameters represent different properties
of the same entity. To learn the hierarchical representation, HGCN
characterizes the part-whole relationship between lower-level capsules (part)
and higher-level capsules (whole) by explicitly considering the structure
information among the parts. Experimental studies demonstrate the effectiveness
of HGCN and the contribution of each component.

30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph