Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Topic Modeling by Incorporating Document Relationship Graph   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.

15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat