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A Label Propagation-Based Algorithm for Community Discovery in Online Social Networks

机译：在线社交网络中基于标签传播的社区发现算法

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摘要

With the rapid development of Internet and Web 2.0 applications, many different patterns of online social networks become fashionable all over the world. These sites help people share and exchange information, as well as maintain their social relations on the Internet. Therefore, it is very important to study the structure of communities in online social network. Most of existed community discovery algorithms are very costly. Moreover, the behavior of users in online social networks is rather dynamic. We first investigate Label Propagation Algorithm (LPA), which has near linear time complexity and discuss some limitations of LPA. Then, we propose a new algorithm for community discovery based on label influence vector (LIVB), an improved variation of LPA. In this algorithm, we abstract several types of nodes corresponding to different kinds of entities such as users, posts, videos as well as comments. Different types of relations between nodes are also taken into account. A node will update its label by calculating its label influence vector. We conduct experiments on crawled real data and the experimental results show that communities discovered by LIVB algorithm have more concentrative topics. The quality of the communities is improved and LIVB algorithm remains a near linear time complexity.
机译：随着Internet和Web 2.0应用程序的飞速发展，在线社交网络的许多不同模式在世界范围内变得流行。这些站点可帮助人们共享和交换信息，并在Internet上维护他们的社会关系。因此，研究在线社交网络中的社区结构非常重要。现有的大多数社区发现算法都非常昂贵。此外，在线社交网络中用户的行为是动态的。我们首先研究标签传播算法（LPA），它具有接近线性的时间复杂度，并讨论了LPA的一些局限性。然后，我们提出了一种基于标签影响向量（LIVB）的新的社区发现算法，该算法是LPA的改进版本。在这种算法中，我们提取了与不同种类的实体（例如用户，帖子，视频以及评论）相对应的几种类型的节点。还考虑了节点之间的不同类型的关系。节点将通过计算其标签影响向量来更新其标签。我们对爬行的真实数据进行了实验，实验结果表明，LIVB算法发现的社区具有更集中的主题。社区的质量得到提高，LIVB算法保持了接近线性的时间复杂度。

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