  Graph Neural Networks (GNNs) have gained great popularity in tackling various
analytical tasks on graph-structured data (i.e., networks). Typical GNNs and
their variants follow a message-passing manner that obtains network
representations by the feature propagation process along network topology,
which however ignore the rich textual semantics (e.g., local word-sequence)
that exist in many real-world networks. Existing methods for text-rich networks
integrate textual semantics by mainly utilizing internal information such as
topics or phrases/words, which often suffer from an inability to
comprehensively mine the text semantics, limiting the reciprocal guidance
between network structure and text semantics. To address these problems, we
propose a novel text-rich graph neural network with external knowledge (TeKo),
in order to take full advantage of both structural and textual information
within text-rich networks. Specifically, we first present a flexible
heterogeneous semantic network that incorporates high-quality entities and
interactions among documents and entities. We then introduce two types of
external knowledge, that is, structured triplets and unstructured entity
description, to gain a deeper insight into textual semantics. We further design
a reciprocal convolutional mechanism for the constructed heterogeneous semantic
network, enabling network structure and textual semantics to collaboratively
enhance each other and learn high-level network representations. Extensive
experimental results on four public text-rich networks as well as a large-scale
e-commerce searching dataset illustrate the superior performance of TeKo over
state-of-the-art baselines.
