  Graph convolution networks, like message passing graph convolution networks
(MPGCNs), have been a powerful tool in representation learning of networked
data. However, when data is heterogeneous, most architectures are limited as
they employ a single strategy to handle multi-channel graph signals and they
typically focus on low-frequency information. In this paper, we present a novel
graph convolution operator, termed BankGCN, which keeps benefits of message
passing models, but extends their capabilities beyond `low-pass' features. It
decomposes multi-channel signals on graphs into subspaces and handles
particular information in each subspace with an adapted filter. The filters of
all subspaces have different frequency responses and together form a filter
bank. Furthermore, each filter in the spectral domain corresponds to a message
passing scheme, and diverse schemes are implemented via the filter bank.
Importantly, the filter bank and the signal decomposition are jointly learned
to adapt to the spectral characteristics of data and to target applications.
Furthermore, this is implemented almost without extra parameters in comparison
with most existing MPGCNs. Experimental results show that the proposed
convolution operator permits to achieve excellent performance in graph
classification on a collection of benchmark graph datasets.
