测试有符号图的聚类属性

Florian Adriaens, Simon Apers
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引用次数: 0

摘要

这项工作开启了符号图中性质检验的研究,其中每条边都有正号或负号。我们证明了有符号图的平衡性(或2-可聚性)、可聚性和有符号三角形自由性这三个关键性质的子线性查询和时间算法。我们考虑密集图模型和有界度模型,前者查询有符号图的邻接矩阵条目,后者查询节点的邻居和连接边的符号。我们的算法使用各种工具,从无符号图属性测试,以及从一种设置到另一种设置的缩减。我们的主要技术贡献是一种用于测试有界度模型的聚类性的次线性算法。这与无符号图的k-聚类性形成对比,无符号图的k-聚类性在有界度模型中不能用次线性的查询次数进行测试。我们通过实验评估了我们在现实生活和合成数据集上的几个测试器的复杂性和有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Testing Cluster Properties of Signed Graphs
This work initiates 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 query and time 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 one queries the adjacency matrix entries of a signed graph, and the bounded-degree model, where one queries for the neighbors of a node and the sign of the connecting edge. Our algorithms use a variety of tools from unsigned 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 in unsigned graphs, which is not testable with a sublinear number of queries in the bounded-degree model. We experimentally evaluate the complexity and usefulness of several of our testers on real-life and synthetic datasets.
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