In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we aim to estimate the target labels by making use of the similarity between the characteristics of the variation of the label functions on the two graphs. Our assumption about the source and the target domains is that the local behaviour of the label function, such as its spread and speed of variation on the graph, bears resemblance between the two graphs. We estimate the unknown target labels by solving an optimization problem where the label information is transferred from the source graph to the target graph based on the prior that the projections of the label functions onto localized graph bases be similar between the source and the target graphs. In order to efficiently capture the local variation of the label functions on the graphs, spectral graph wavelets are used as the graph bases. Experimentation on various data sets shows that the proposed method yields quite satisfactory classification accuracy compared to reference domain adaptation methods.
翻译:在本文中,我们建议了为图形域设计的域适应算法。根据含有许多标签节点的源图和带有很少或没有标签节点的目标图,我们的目标是利用两个图表标签函数变化特性的相似性来估计目标标签。我们对源和目标域的假设是,标签函数的本地行为,如其分布和图上变化速度等,与两个图表相似。我们通过解决一个优化问题来估计未知的目标标签标签,即标签信息从源图转移到基于源和目标图的前一个目标图,即对本地图形基点的标签功能的预测类似于源和目标图。为了高效地捕捉图上标签功能的本地变化,将光谱图波子波子用作图形基础。对各种数据集的实验表明,拟议的方法与参考域适应方法相比,可以产生相当令人满意的分类准确性。