社交网络自适应播种的可扩展方法
Scalable Methods for Adaptively Seeding a Social Network
AI总结：
本文针对社交网络中影响力受限用户的信息传播问题，提出一种可扩展的自适应播种方法，为线性影响模型开发可并行化的近似算法，实验证明其效果显著优于标准传播方法。
摘要：
近年来，社交网络平台已发展成为传播和消费信息的重要渠道。随着此类基础设施的兴起，通过有影响力的用户有效传播信息的技术也在不断进步。在许多应用中，人们只能从参与过所推广话题的用户集合中选择影响者，并且由于社交网络的结构，这些用户在影响力潜力方面往往排名较低。人们可以考虑的一种替代方法是自适应方法，该方法以针对用户的有影响力邻居的方式来选择用户。这种方法的优势在于它利用了社交网络中的友谊悖论：虽然用户往往没有影响力，但他们通常认识有影响力的人。尽管此类优化问题存在各种复杂性，我们表明可扩展的自适应播种是可实现的。具体而言，我们为线性影响模型开发了算法，具有可证明的近似保证，并且可以优雅地并行化。为了展示我们方法的有效性，我们收集了社交网络用户关注的各种垂直领域的数据。对于每个垂直领域，我们收集了对特定帖子做出响应的用户及其邻居的数据，并将我们的方法应用于这些数据。我们的实验表明，自适应播种具有可扩展性，且重要的是，它比标准的信息传播方法获得了显著的改进。
英文摘要：
In recent years, social networking platforms have developed into extraordinary channels for spreading and consuming information. Along with the rise of such infrastructure, there is continuous progress on techniques for spreading information effectively through influential users. In many applications, one is restricted to select influencers from a set of users who engaged with the topic being promoted, and due to the structure of social networks, these users often rank low in terms of their influence potential. An alternative approach one can consider is an adaptive method which selects users in a manner which targets their influential neighbors. The advantage of such an approach is that it leverages the friendship paradox in social networks: while users are often not influential, they often know someone who is. Despite the various complexities in such optimization problems, we show that scalable adaptive seeding is achievable. In particular, we develop algorithms for linear influence models with provable approximation guarantees that can be gracefully parallelized. To show the effectiveness of our methods we collected data from various verticals social network users follow. For each vertical, we collected data on the users who responded to a certain post as well as their neighbors, and applied our methods on this data. Our experiments show that adaptive seeding is scalable, and importantly, that it obtains dramatic improvements over standard approaches of information dissemination.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
