  Graph convolutional networks (GCNs), aiming to integrate high-order
neighborhood information through stacked graph convolution layers, have
demonstrated remarkable power in many network analysis tasks. However,
topological limitations, including over-smoothing and local topology homophily,
limit its capability to represent networks. Existing studies only perform
feature convolution on network topology, which inevitably introduces unbalance
between topology and features. Considering that in real world, the information
network consists of not only the node-level citation information but also the
local text-sequence information. We propose BiTe-GCN, a novel GCN architecture
with bidirectional convolution of both topology and features on text-rich
networks to solve these limitations. We first transform the original text-rich
network into an augmented bi-typed heterogeneous network, capturing both the
global node-level information and the local text-sequence information from
texts. We then introduce discriminative convolution mechanisms to performs
convolutions of both topology and features simultaneously. Extensive
experiments on text-rich networks demonstrate that our new architecture
outperforms state-of-the-art by a breakout improvement. Moreover, this
architecture can also be applied to several e-commerce searching scenes such as
JD searching. The experiments on the JD dataset validate the superiority of the
proposed architecture over the related methods.
