Liu Rui, Cheng Wei, Tong Hanghang, Wang Wei, Zhang Xiang
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106.
Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599.
Proc IEEE Int Conf Data Min. 2015 Nov;2015:291-300. doi: 10.1109/ICDM.2015.13.
Network clustering is an important problem that has recently drawn a lot of attentions. Most existing work focuses on clustering nodes within a single network. In many applications, however, there exist networks, in which each network may be constructed from a different domain and instances in one domain may be related to instances in other domains. In this paper, we propose a robust algorithm, MCA, for multi-network clustering that takes into account cross-domain relationships between instances. MCA has several advantages over the existing single network clustering methods. First, it is able to detect associations between clusters from different domains, which, however, is not addressed by any existing methods. Second, it achieves more consistent clustering results on multiple networks by leveraging the between clustering individual networks and inferring cross-network cluster alignment. Finally, it provides a multi-network clustering solution that is more robust to noise and errors. We perform extensive experiments on a variety of real and synthetic networks to demonstrate the effectiveness and efficiency of MCA.
网络聚类是一个重要问题，最近受到了很多关注。大多数现有工作集中于对单个网络内的节点进行聚类。然而，在许多应用中，存在多个网络，其中每个网络可能由不同领域构建，并且一个领域中的实例可能与其他领域中的实例相关。在本文中，我们提出了一种用于多网络聚类的稳健算法MCA，该算法考虑了实例之间的跨域关系。与现有的单网络聚类方法相比，MCA有几个优点。首先，它能够检测来自不同领域的簇之间的关联，而现有方法均未涉及这一点。其次，通过利用单个网络聚类之间的关系并推断跨网络簇对齐，它在多个网络上实现了更一致的聚类结果。最后，它提供了一种对噪声和错误更具鲁棒性的多网络聚类解决方案。我们在各种真实和合成网络上进行了广泛实验，以证明MCA的有效性和效率。
