论文标题
图形上的半监督节点分类：马尔可夫随机字段与图形神经网络
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
论文作者
论文摘要
图形结构化数据上的半监督节点分类具有许多应用程序，例如欺诈检测，伪造帐户和审查检测，用户在社交网络中的私人属性推断以及社区检测。为半监督节点分类开发了各种方法，例如成对Markov随机场（PMRF）和图神经网络。 PMRF比图神经网络更有效。但是，由于关键的限制，现有的基于PMRF的方法比图神经网络的准确性不如图形神经网络。在这项工作中，我们旨在解决现有基于PMRF的方法的关键限制。特别是，我们建议学习PMRF的边缘潜力。我们对各种图形数据集的评估结果表明，我们基于PMRF的优化方法在准确性和效率方面始终优于现有的图形神经网络。我们的结果表明，以前的工作可能低估了PMRF对于半监督节点分类的功率。
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.
