In graph property testing the task is to distinguish whether a graph satisfies a given property or is "far" from having that property, preferably with a sublinear query and time complexity. In this work we initiate the study of property testing in signed graphs, where every edge has either a positive or a negative sign. We show that there exist sublinear algorithms for testing three key properties of signed graphs: balance (or 2-clusterability), clusterability and signed triangle freeness. We consider both the dense graph model, where we can query the (signed) adjacency matrix of a signed graph, and the bounded-degree model, where we can query for the neighbors of a node and the sign of the connecting edge. Our algorithms use a variety of tools from graph property testing, as well as reductions from one setting to the other. Our main technical contribution is a sublinear algorithm for testing clusterability in the bounded-degree model. This contrasts with the property of k-clusterability which is not testable with a sublinear number of queries. The tester builds on the seminal work of Goldreich and Ron for testing bipartiteness.
翻译:在图形属性测试中,任务在于区分图形是否满足给定属性,还是“远远”不具有该属性,最好是次线性查询和时间复杂性。在这项工作中,我们开始在每个边缘都有正或负符号的签名图表中进行属性测试研究。我们显示存在用于测试签名图形的三个主要属性的子线性算法:平衡(或2组),可集成性和签名三角自由性。我们既考虑密集的图形模型,我们在那里可以查询已签名图形的(签名的)相邻矩阵,也考虑界度模型,我们可以在此中查询节点的近邻和连接边缘的标志。我们的算法使用了图性属性测试中的多种工具,以及从一个设置到另一个设置的减值。我们的主要技术贡献是用于测试约束度模型中的可集集成性的亚线性算法。这与K组的属性是对比的,无法用子线性查询数测试。测试者以Goldreich和Ron的半线性工作为基础,测试两边端。