网络分类的多跳迁移率
Multi-hop assortativities for networks classification
摘要：
一些社会、医学、工程和生物学挑战依赖于从网络的结构和节点元数据中发现网络的功能。例如，在化学信息学中，人们可能希望根据结构和原子类型检测分子是否有毒，或者发现科学合作网络的研究领域。现有技术依赖于计算或测量已知显示网络之间的大变化的结构模式，例如三角形的数量或节点元数据的重复性。我们引入了多跳重复性的概念，它捕获了位于给定长度的随机选择的路径的端点处的节点的相似性。我们表明，多跳的可追溯性统一了各种现有的概念，并提供了一个通用的家庭的“指纹”来表征网络。在机器学习工具箱的帮助下，这些指纹又允许恢复网络的功能。我们的方法是根据既定的社会和化学信息网络基准进行经验评估的。结果表明，我们的自主性为基础的功能是有竞争力的，提供高度准确的结果往往优于最先进的方法的网络分类任务。
英文摘要：
Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of 'fingerprints' to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.
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