社交网络用户影响力分析ABP算法研究与应用

社交网络作为一种交往方式,已经深入人心。其用户数据在这个大数据时代蕴藏着大量的价值。随着Twitter API的开放,社交网络Twitter俨然成为一个深受欢迎的研究对象,而用户影响力更是其中的研究热点。PageRank算法计算用户影响力已经由来已久,但是它太依赖于用户之间的关注关系,排名不具备时效性。引入用户活跃度的改进PageRank算法,具备一定的时效性,但是不具有足够的说服力和准确性。研究了一种新的基于时间分布用户活跃度的ABP算法,并为不同时段的活跃度加以相应的时效权重因子。最后,以Twitter为研究对象,结合社交关系网,通过实例分析说明ABP算法更具时效性和说服力,可以比较准确地提高活跃用户的排名,降低非活跃用户排名。

An ABP algorithm for user influence analysis in social networks

As a means of communication, social networks have taken root in people’s hearts. The user data of social networks has a lot of value in this big data era. With the opening of Twitter Application Programming Interface (API), Twitter, as a social networking site, has become a popular research object, especially the user influence. The PageRank algorithm has long been in use to calculate users’ influence, however, it is too dependent on the following relationship between users, so the ranking of users does not have strong timeliness. We introduce user activity to improve the PageRank algorithm, which has a certain degree of timeliness, but not convincing and accurate. We propose a new algorithm called PageRank activity based (ABP) algorithm according to the time distribution of user activity, and corresponding ageing weight factors are applied to the active degree of different periods of time. Finally we taking Twitter as the research object and combining with the social relationship graph, we prove that the ABP algorithm is more efficient and persuasive through an example analysis, and it can be more accurate in improving the ranking of active users and reducing the ranking of inactive users.