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Dynamic top-k influence maximization in social networks
GeoInformatica ( IF 2 ) Pub Date : 2020-07-24 , DOI: 10.1007/s10707-020-00419-6
Binbin Zhang , Hao Wang , Leong Hou U

The problem of top-k influence maximization is to find the set of k users in a social network that can maximize the spread of influence under certain influence propagation model. This paper studies the influence maximization problem together with network dynamics. For example, given a real-life social network that evolves over time, we want to find k most influential users on everyday basis. This dynamic influence maximization problem has wide applications in practice. However, to our best knowledge, there is little prior work that studies this problem. Applying existing influence maximization algorithms at every time step provides a straightforward solution to the dynamic top-k influence maximization problem. Such a solution is, however, inefficient as it completely ignores the smoothness of network change. By analyzing two real social networks, Brightkite and Gowalla, we observe that the top-k influential set, as well as its influence value, does not change dramatically over time. Hence, it is possible to find the new top-k influential set by updating the previous one. We propose an efficient incremental update framework that takes advantage of such smoothness of network change. The proposed method achieves the same approximation ratio of 1 − e− 1 as its state-of-the-art static counterparts. Our experiments show that the proposed method outperforms the straightforward solution by a wide margin.



中文翻译:

社交网络中动态的top-k影响力最大化

top- k影响力最大化的问题是在社交网络中找到可以在特定影响力传播模型下最大化影响力传播的k个用户的集合。本文结合网络动力学研究影响最大化的问题。例如,假设现实生活中的社交网络随着时间而发展,我们希望每天找到k个最具影响力的用户。动态影响最大化问题在实践中具有广泛的应用。但是,据我们所知,很少有研究此问题的先前工作。在每个时间步骤应用现有影响力最大化算法可为动态Top- k提供直接的解决方案影响最大化问题。但是,这种解决方案效率低下,因为它完全忽略了网络更改的平稳性。通过分析两个实际的社交网络,Brightkite和Gowalla,我们观察到前k个影响力集及其影响值不会随时间发生显着变化。因此,有可能通过更新先前的前k个有影响的集合。我们提出了一种有效的增量更新框架,该框架利用了网络变化的这种平滑性。所提出的方法实现了与最新技术的静态对应物相同的近似比1- e -1。我们的实验表明,所提出的方法在很大程度上优于直接方法。

更新日期:2020-07-24
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