跨域网络表示
Cross-domain Network Representations
摘要：
网络表示的目的是通过从网络结构中获取社区信息来学习一组潜在特征，为机器学习任务提供知识。最近的研究通过采用随机游走作为网络采样策略，推动了网络表示的重大进展。然而，现有的方法依赖于域特定的丰富的社区结构，并在网络中缺乏拓扑信息，在自己的域失败。在本文中，我们提出了一种新的算法跨域网络表示，命名为CDNR。通过从结构丰富的域生成随机游走，并跨域转移关于随机游走的知识，它也能够实现结构稀缺域的网络表示。具体而言，CDNR是在跨领域两层随机游走学习框架下，通过跨领域两层节点尺度平衡算法和跨领域两层知识转移算法实现的。在各种真实世界数据集上的实验证明了CDNR在无监督方式下对通用网络的有效性。
英文摘要：
The purpose of network representation is to learn a set of latent features by obtaining community information from network structures to provide knowledge for machine learning tasks. Recent research has driven significant progress in network representation by employing random walks as the network sampling strategy. Nevertheless, existing approaches rely on domain-specifically rich community structures and fail in the network that lack topological information in its own domain. In this paper, we propose a novel algorithm for cross-domain network representation, named as CDNR. By generating the random walks from a structural rich domain and transferring the knowledge on the random walks across domains, it enables a network representation for the structural scarce domain as well. To be specific, CDNR is realized by a cross-domain two-layer node-scale balance algorithm and a cross-domain two-layer knowledge transfer algorithm in the framework of cross-domain two-layer random walk learning. Experiments on various real-world datasets demonstrate the effectiveness of CDNR for universal networks in an unsupervised way.
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。
