标题： 跨多个部分对齐的社会网络的互惠社区检测 显示英文标题

标题： Mutual Community Detection across Multiple Partially Aligned Social Networks

摘要： 在在线社交网络中的社区检测近年来已成为一个热门的研究课题。 同时，为了享受更多的社交网络服务，用户现在通常同时参与多个在线社交网络，其中一些网络可以共享共同的信息和结构。 涉及一些共同用户的网络被称为多个“部分对齐的网络”。 在本文中，我们希望同时检测多个部分对齐网络的社区，这被正式定义为“互聚类”问题。 “互聚类”问题非常具有挑战性，因为它有两个重要的问题需要解决：(1) 如何在互社区检测中保留网络特征？以及 (2) 如何利用其他对齐网络中的信息来优化和消除共享用户社区结构的歧义？ 为了解决这两个挑战，本文提出了一种新的社区检测方法，MCD（互社区检测器）。 MCD可以在充分考虑(1)每个网络的特征，以及(2)对齐网络中共享用户的跨网络信息的情况下，同时检测多个部分对齐网络中用户的社交社区结构。 在两个真实世界的部分对齐异构社交网络上进行的大量实验表明，MCD能够很好地解决“互聚类”问题。 显示英文摘要

摘要： Community detection in online social networks has been a hot research topic in recent years. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously, some of which can share common information and structures. Networks that involve some common users are named as multiple "partially aligned networks". In this paper, we want to detect communities of multiple partially aligned networks simultaneously, which is formally defined as the "Mutual Clustering" problem. The "Mutual Clustering" problem is very challenging as it has two important issues to address: (1) how to preserve the network characteristics in mutual community detection? and (2) how to utilize the information in other aligned networks to refine and disambiguate the community structures of the shared users? To solve these two challenges, a novel community detection method, MCD (Mutual Community Detector), is proposed in this paper. MCD can detect social community structures of users in multiple partially aligned networks at the same time with full considerations of (1) characteristics of each network, and (2) information of the shared users across aligned networks. Extensive experiments conducted on two real-world partially aligned heterogeneous social networks demonstrate that MCD can solve the "Mutual Clustering" problem very well.