Fuzzy Neural Network for Representation Learning on Uncertain Graphs

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.