有向网络的双曲嵌入模型
A hyperbolic Embedding Model for Directed Networks
摘要：
网络嵌入是当前网络科学中的一个热门话题，它认为大多数真实的复杂系统都可以嵌入到隐度量空间中，并以几何属性的形式出现，其中节点之间的几何距离决定了链路连接的可能性。其中，双曲空间与许多实际复杂系统的结构组织相联系，因而受到广泛的关注。然而，大多数的方法和测量，最近开发的，很少考虑这些功能的不对称的链接。本文讨论了如何通过识别有向网络的二分结构来复用节点信息作为嵌入的基础，并提出了一种融合复杂网络拓扑结构、有向链路和隐度量空间的通用映射框架。通过分割节点的不同属性，可以对不同类型节点之间的可能性进行建模。此外，我们还将该模型应用到一些实际系统中，包括国际贸易网络和秀丽隐杆线虫神经网络。结果表明，有向网络也可以映射到度量空间，网络嵌入信息可以提高现有模型的应用范围。
英文摘要：
Network embedding is a fervid topic in current networks science and observes that most real complex systems can be embedded in hidden metrics space and emerge as the geometrical property, where the geometric distance between nodes determines the likelihood of links connected. Among those, hyperbolic space associated with the structural organization of many real complex systems, it has thus received extensive attention. However, the majority of methods and measurements, recently developed, less take these features into consideration for the asymmetry of links. Here, we discuss how to multiplex node information as an embedding foundation through identifying the bipartite structure of directed networks; and we proposed the generally mapping framework which hybrids the topological structure of complex networks, directed links and the hidden metrics space. By splitting the different properties of a node, possibilities between different types of nodes can be modeled. In addition to that, we apply this model to some real systems, including international trade networks and C.elegans neural networks. Results confirm that directed networks enable mapping into metrics space as well, and network embedding information can improve the scope of application of existing models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
