  Graph Neural Networks (GNNs) that capture the relationships between graph
nodes via message passing have been a hot research direction in the natural
language processing community. In this paper, we propose Graph Topic Model
(GTM), a GNN based neural topic model that represents a corpus as a document
relationship graph. Documents and words in the corpus become nodes in the graph
and are connected based on document-word co-occurrences. By introducing the
graph structure, the relationships between documents are established through
their shared words and thus the topical representation of a document is
enriched by aggregating information from its neighboring nodes using graph
convolution. Extensive experiments on three datasets were conducted and the
results demonstrate the effectiveness of the proposed approach.
