Graph representation learning focuses on abstracting critical information from raw graphs. Unfortunately, there always exist various kinds of uncertainties, such as attribute noise and network topology corruption, in raw graphs. Under the message passing mechanism, certainties are likely to spread throughout the whole graph. Matters like these would induce deep graph models into producing uncertain representations and restrict representation expressiveness. Considering this, we propose a pioneering framework to defend graph uncertainties by improving the robustness and capability of graph neural networks (GNNs). In our framework, we consider that weights and biases are all fuzzy numbers, thus generating representations to assimilate graph uncertainties, which are finally released by defuzzification. To describe the process of the framework, in this article, a graph convolutional network (GCN) is employed to construct a robust graph model, called FuzzyGCN. To verify the effectiveness of FuzzyGCN, it is trained in both supervised and unsupervised ways. In the supervised setting, we find that FuzzyGCN has stronger power and is more immune to data uncertainties when compared with various classical and robust GNNs. In the unsupervised setting, FuzzyGCN surpasses many state-of-the-art models in node classification and community detection over several real-world datasets.

中文翻译：

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用于不确定图表示学习的模糊神经网络


图表示学习的重点是从原始图中提取关键信息。不幸的是，原始图中始终存在各种不确定性，例如属性噪声和网络拓扑损坏。在消息传递机制下，确定性很可能遍布整个图。诸如此类的问题会导致深度图模型产生不确定的表示并限制表示的表达能力。考虑到这一点，我们提出了一个开创性的框架，通过提高图神经网络（GNN）的鲁棒性和能力来防御图的不确定性。在我们的框架中，我们认为权重和偏差都是模糊数，从而生成表示来吸收图的不确定性，最终通过去模糊化来释放这些不确定性。为了描述该框架的过程，在本文中，采用图卷积网络（GCN）来构建鲁棒的图模型，称为FuzzyGCN。为了验证 FuzzyGCN 的有效性，它以有监督和无监督的方式进行训练。在监督环境中，我们发现与各种经典和鲁棒的 GNN 相比，FuzzyGCN 具有更强的能力，并且更容易受到数据不确定性的影响。在无监督环境中，FuzzyGCN 在几个现实世界数据集的节点分类和社区检测方面超越了许多最先进的模型。