  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.
