Gera Ralucca, Alonso L, Crawford Brian, House Jeffrey, Mendez-Bermudez J A, Knuth Thomas, Miller Ryan
2Department of Applied Mathematics, 1 University Avenue, Naval Postgraduate School, Monterey, 93943 CA USA.
1Instituto de Física, Benemérita Universidad Autónoma de Puebla, Apartado Postal J-48, Puebla, 72570 Mexico.
Appl Netw Sci. 2018;3(1):2. doi: 10.1007/s41109-017-0042-3. Epub 2018 Jan 31.
Most real networks are too large or they are not available for real time analysis. Therefore, in practice, decisions are made based on partial information about the ground truth network. It is of great interest to have metrics to determine if an inferred network (the partial information network) is similar to the ground truth. In this paper we develop a test for similarity between the inferred and the true network. Our research utilizes a network visualization tool, which systematically discovers a network, producing a sequence of snapshots of the network. We introduce and test our metric on the consecutive snapshots of a network, and against the ground truth. To test the scalability of our metric we use a random matrix theory approach while discovering Erdös-Rényi graphs. This scaling analysis allows us to make predictions about the performance of the discovery process.
大多数实际网络规模太大，或者无法进行实时分析。因此，在实践中，决策是基于关于真实网络的部分信息做出的。拥有能够确定推断网络（部分信息网络）是否与真实网络相似的指标非常重要。在本文中，我们开发了一种用于推断网络与真实网络之间相似性的测试方法。我们的研究利用了一种网络可视化工具，该工具系统地发现一个网络，生成该网络的一系列快照。我们在网络的连续快照上并与真实网络进行对比，引入并测试我们的指标。为了测试我们指标的可扩展性，我们在发现厄多斯 - 雷尼图时使用随机矩阵理论方法。这种缩放分析使我们能够对发现过程的性能做出预测。
