一种基于增广网络的快速微博社区检测算法

微博是当前最流行的在线社交媒体之一,有效地检测出微博用户的社区结构,能够帮助人们理解微博社交网络的结构和用户的行为特征,从而为用户提供个性化的服务。然而,现有社区检测算法大多只考虑社交网络节点之间的直接链接关系,忽略节点自身的内容特征。针对此问题,提出一种基于增广网络的快速微博社区检测算法。该算法通过融合社交网络的链接信息以及用户在微博上所发布的博文内容信息构建增广网络,然后以模块度为目标函数快速挖掘增广网络中的主题社区。通过真实微博社交网络的实验表明,提出的算法能够高效地检测出社交网络的主题社区。

A Fast Microblog Community Detection Algorithm Based on Augmented Network

Microblog is one of the most popular online social media nowadays. Identification of users' community structure on Microblog can help people understand the community structure as well as users' behaviors, and even provide personalized service for users. Currently, most of the studies on Microblog community detection algorithm focus on the link information, ignoring the information posted by users. To address this issue, a fast Microblog community detection algorithm based on augmented network is proposed. The algorithm constructs an augmented network by integrating users' link information and content, on which community can be identified efficiently. Experimental results show that the proposed algorithm performs better in identifying the community structure of social networks in real Microblog network when compared with other algorithms.