Toward finding hidden communities based on user profile
Toward finding hidden communities based on user profile
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DOI：
10.1007/s10844-011-0175-2
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发表时间：
2010-12
影响因子：
3.4
通讯作者：
Tetsuya Yoshida
中科院分区：
文献类型：
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作者：
Tetsuya Yoshida
我们认为社区检测问题，从一个部分可观察的网络结构，其中一些边缘是不可观察的。以前的社区检测方法往往只基于观察到的连接关系，并没有明确考虑上述情况。即使当连接关系是部分可观察的，如果一些轮廓数据的顶点在网络中是可用的，它可以被利用作为辅助或额外的信息。我们建议利用一个图形结构（称为配置文件图），这是通过配置文件数据构建的，并提出了一个简单的模型，利用观察到的连接关系和配置文件图。此外，而不是一个层次化的方法，基于网络结构的模块矩阵，我们提出了一种嵌入方法，利用正则化通过剖面图。在两个社交网络数据集上进行了各种实验，并与几种最先进的方法进行了比较。结果是令人鼓舞的，并表明，这是有希望的，以追求这方面的研究。
We consider the community detection problem from a partially observable network structure where some edges are not observable. Previous community detection methods are often based solely on the observed connectivity relation and the above situation is not explicitly considered. Even when the connectivity relation is partially observable, if some profile data about the vertices in the network is available, it can be exploited as auxiliary or additional information. We propose to utilize a graph structure (called a profile graph) which is constructed via the profile data, and propose a simple model to utilize both the observed connectivity relation and the profile graph. Furthermore, instead of a hierarchical approach, based on the modularity matrix of the network structure, we propose an embedding approach which utilizes the regularization via the profile graph. Various experiments are conducted over two social network datasets and comparison with several state-of-the-art methods is reported. The results are encouraging and indicate that it is promising to pursue this line of research.