Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
This work provides a more accurate and efficient method for semi-supervised node classification, which is significant for applications like fraud detection and community detection, by improving upon an existing paradigm.
This paper addresses the limitation of existing pairwise Markov Random Fields (pMRF) methods for semi-supervised node classification, which assume a constant edge potential. By learning edge potentials, their optimized pMRF method consistently outperforms existing graph neural networks in both accuracy and efficiency across various graph datasets.
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.