基于多层数据的网络合成
Network Composition from Multi-layer Data
摘要：
人们通常访问多个社交网络，例如，使用电话、电子邮件和社交媒体。多层次的社会互动共同构成了一个“综合社会网络”。“我们如何将关于单层网络的成熟知识，包括顶点中心性和社区结构，扩展到这种异构结构？在本文中，我们提出了一个原则性的框架，网络组成的基础上，一个统一的动态过程来处理这些挑战。在数学上，我们考虑以下抽象问题：给定多层网络数据和层内和层间动态的附加参数，构建一个最佳集成联合过程的（单个）加权网络。我们使用动力学的转换，统一异构层下一个共同的动力学。对于层间组成，我们将考虑几种情况，因为层间动态在各种社会或技术网络中扮演不同的角色。从经验上讲，我们提供的例子来强调网络分析和网络设计的有用性，这个框架。
英文摘要：
It is common for people to access multiple social networks, for example, using phone, email, and social media. Together, the multi-layer social interactions form a "integrated social network." How can we extend well developed knowledge about single-layer networks, including vertex centrality and community structure, to such heterogeneous structures? In this paper, we approach these challenges by proposing a principled framework of network composition based on a unified dynamical process. Mathematically, we consider the following abstract problem: Given multi-layer network data and additional parameters for intra and inter-layer dynamics, construct a (single) weighted network that best integrates the joint process. We use transformations of dynamics to unify heterogeneous layers under a common dynamics. For inter-layer compositions, we will consider several cases as the inter-layer dynamics plays different roles in various social or technological networks. Empirically, we provide examples to highlight the usefulness of this framework for network analysis and network design.
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。
