  Learning hidden topics from data streams has become absolutely necessary but
posed challenging problems such as concept drift as well as short and noisy
data. Using prior knowledge to enrich a topic model is one of potential
solutions to cope with these challenges. Prior knowledge that is derived from
human knowledge (e.g. Wordnet) or a pre-trained model (e.g. Word2vec) is very
valuable and useful to help topic models work better. However, in a streaming
environment where data arrives continually and infinitely, existing studies are
limited to exploiting these resources effectively. Especially, a knowledge
graph, that contains meaningful word relations, is ignored. In this paper, to
aim at exploiting a knowledge graph effectively, we propose a novel graph
convolutional topic model (GCTM) which integrates graph convolutional networks
(GCN) into a topic model and a learning method which learns the networks and
the topic model simultaneously for data streams. In each minibatch, our method
not only can exploit an external knowledge graph but also can balance the
external and old knowledge to perform well on new data. We conduct extensive
experiments to evaluate our method with both a human knowledge graph (Wordnet)
and a graph built from pre-trained word embeddings (Word2vec). The experimental
results show that our method achieves significantly better performances than
state-of-the-art baselines in terms of probabilistic predictive measure and
topic coherence. In particular, our method can work well when dealing with
short texts as well as concept drift. The implementation of GCTM is available
at \url{https://github.com/bachtranxuan/GCTM.git}.
