Jiang He, He Haibo
IEEE Trans Cybern. 2022 Aug;52(8):8481-8492. doi: 10.1109/TCYB.2021.3104246. Epub 2022 Jul 19.
Recently, graph convolutional networks (GCNs) and their variants have achieved remarkable successes for the graph-based semisupervised node classification problem. With a GCN, node features are locally smoothed based on the information aggregated from their neighborhoods defined by the graph topology. In most of the existing methods, the graph typologies only contain positive links which are deemed as descriptions for the feature similarity of connected nodes. In this article, we develop a novel GCN-based learning framework that improves the node representation inference capability by including negative links in a graph. Negative links in our method define the inverse correlations for the nodes connected by them and are adaptively generated through a neural-network-based generation model. To make the generated negative links beneficial for the classification performance, this negative link generation model is jointly optimized with the GCN used for class inference through our designed training algorithm. Experiment results show that the proposed learning framework achieves better or matched performance compared to the current state-of-the-art methods on several standard benchmark datasets.
最近，图卷积网络（GCN）及其变体在基于图的半监督节点分类问题上取得了显著成功。使用GCN时，节点特征会根据从由图拓扑定义的邻域聚合的信息进行局部平滑。在大多数现有方法中，图类型仅包含正链接，这些正链接被视为对相连节点特征相似性的描述。在本文中，我们开发了一种基于GCN的新型学习框架，该框架通过在图中纳入负链接来提高节点表示推理能力。我们方法中的负链接定义了由它们连接的节点之间的反相关性，并通过基于神经网络的生成模型自适应生成。为了使生成的负链接对分类性能有益，这个负链接生成模型通过我们设计的训练算法与用于类推理的GCN联合优化。实验结果表明，与当前在几个标准基准数据集上的最先进方法相比，所提出的学习框架实现了更好或相当的性能。
