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.

本文介绍了一种名为 Graph Topic Model (GTM) 的基于图形神经网络 (GNNs) 的神经主题模型，通过信息交互捕捉图形节点之间的关系，将文档和词汇转化成为图中的节点，并根据共现性连接，通过图卷积汇聚邻居节点信息从而通过共享单词在文档之间建立关系，扩充了文档的主题的表达方式。研究者还基于三个数据集开展了广泛的实验，其结果证明了这种方法的有效性。