Convex Relaxation for Community Detection with Covariates
Convex Relaxation for Community Detection with Covariates
复制标题
使用协变量进行社区检测的凸松弛
DOI：
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复制
发表时间：
2016
期刊：
影响因子：
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通讯作者：
Purnamrita Sarkar
中科院分区：
文献类型：
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作者：
Bowei Yan;Purnamrita Sarkar
网络中的社区发现是许多应用领域中的一个重要问题。在本文中，我们研究这在节点协变量的存在下。最近，一系列新兴的理论工作集中在利用来自网络边缘和节点协变量的信息来推断社区成员资格。然而，到目前为止，网络的作用和协变量的作用还没有得到仔细研究。本质上，在大多数参数机制中，信息源之一提供足够的信息来推断隐藏的聚类标签，从而使另一个源冗余。据我们所知，这是第一个工作表明，当网络和协变量携带“正交”的集群成员信息时，可以通过使用它们两者来获得渐近一致的聚类，而它们中的每一个单独失败。
Community detection in networks is an important problem in many applied areas. In this paper, we investigate this in the presence of node covariates. Recently, an emerging body of theoretical work has been focused on leveraging information from both the edges in the network and the node covariates to infer community memberships. However, so far the role of the network and that of the covariates have not been examined closely. In essence, in most parameter regimes, one of the sources of information provides enough information to infer the hidden cluster labels, thereby making the other source redundant. To our knowledge, this is the first work which shows that when the network and the covariates carry "orthogonal" pieces of information about the cluster memberships, one can get asymptotically consistent clustering by using them both, while each of them fails individually.
DOI：
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发表时间：
2018
期刊：
Proceedings of the International Conference on Artificial Intelligence and Statistics
影响因子：
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作者：
Yan, B.;Sarkar, P.;Cheng, X.
通讯作者：
Cheng, X.