The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering. In this paper, we introduce a graph recurrent neural network (GRNN) for scalable semi-supervised learning from multi-relational data. Key aspects of the novel GRNN architecture are the use of multi-relational graphs, the dynamic adaptation to the different relations via learnable weights, and the consideration of graph-based regularizers to promote smoothness and alleviate over-parametrization. Our ultimate goal is to design a powerful learning architecture able to: discover complex and highly non-linear data associations, combine (and select) multiple types of relations, and scale gracefully with respect to the size of the graph. Numerical tests with real data sets corroborate the design goals and illustrate the performance gains relative to competing alternatives.
翻译:数据巨量时代激发了社会学、生物学、神经科学或工程学等学科对基于图表的学习方法的兴趣。 在本文中,我们引入了一张图表经常性神经网络(GNN),以便从多关系数据中进行可缩放的半监督的学习。新的GNN结构的关键方面是使用多关系图,通过可学习的重量对不同关系进行动态适应,以及考虑采用基于图表的正规化器,以促进平稳和缓解过度平衡。我们的最终目标是设计一个强大的学习结构,能够:发现复杂和高度非线性的数据协会,组合(和选择)多种类型的关系,并根据图表的大小进行宽度。用真实数据集进行的数字测试证实了设计目标,并说明了与竞争性替代品相比的绩效收益。