  Existing NTMs with contrastive learning suffer from the sample bias problem
owing to the word frequency-based sampling strategy, which may result in false
negative samples with similar semantics to the prototypes. In this paper, we
aim to explore the efficient sampling strategy and contrastive learning in NTMs
to address the aforementioned issue. We propose a new sampling assumption that
negative samples should contain words that are semantically irrelevant to the
prototype. Based on it, we propose the graph contrastive topic model (GCTM),
which conducts graph contrastive learning (GCL) using informative positive and
negative samples that are generated by the graph-based sampling strategy
leveraging in-depth correlation and irrelevance among documents and words. In
GCTM, we first model the input document as the document word bipartite graph
(DWBG), and construct positive and negative word co-occurrence graphs (WCGs),
encoded by graph neural networks, to express in-depth semantic correlation and
irrelevance among words. Based on the DWBG and WCGs, we design the
document-word information propagation (DWIP) process to perform the edge
perturbation of DWBG, based on multi-hop correlations/irrelevance among
documents and words. This yields the desired negative and positive samples,
which will be utilized for GCL together with the prototypes to improve learning
document topic representations and latent topics. We further show that GCL can
be interpreted as the structured variational graph auto-encoder which maximizes
the mutual information of latent topic representations of different
perspectives on DWBG. Experiments on several benchmark datasets demonstrate the
effectiveness of our method for topic coherence and document representation
learning compared with existing SOTA methods.
