Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Node Classification on Graphs: Markov Random Fields vs.
  Graph Neural Networks   Semi-supervised node classification on graph-structured data has many
applications such as fraud detection, fake account and review detection, user's
private attribute inference in social networks, and community detection.
Various methods such as pairwise Markov Random Fields (pMRF) and graph neural
networks were developed for semi-supervised node classification. pMRF is more
efficient than graph neural networks. However, existing pMRF-based methods are
less accurate than graph neural networks, due to a key limitation that they
assume a heuristics-based constant edge potential for all edges. In this work,
we aim to address the key limitation of existing pMRF-based methods. In
particular, we propose to learn edge potentials for pMRF. Our evaluation
results on various types of graph datasets show that our optimized pMRF-based
method consistently outperforms existing graph neural networks in terms of both
accuracy and efficiency. Our results highlight that previous work may have
underestimated the power of pMRF for semi-supervised node classification.

28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat