近年来随着新浪微博、人人网等社交网络新媒体的涌现,线上影响力传播得到了越来越多企业和研究机构的关注。如何在给定资源的约束下实现最大的传播范围(影响力最大化问题),对病毒营销等市场战略的有效开展有着重要意义。如果能充分利用社交网络上的异质性信息来更准确地定位用户所属的领域,进而基于领域实现影响力最大化,将对从整体角度出发的传统研究和片面的结构或内容角度的研究形成很好的补充。该文同时利用新浪微博上用户之间的社交关系和微博内容的话题两个维度的信息将用户划分为不同的领域;进而提出了一种基于贪心和动态规划混合的改良算法实现基于领域的影响力最大化。实验表明该文的领域影响力模型较好优化了传统影响力最大化的时间消耗,同时拥有相近的精度。
Abstract
With the evolvement of social media like Sina Weibo and Renren, online influence maximization has attracted more and more attention from both industry and research institutions. Its very crucial for the healthy development of marketing strategies like viral marketing to maximize the influence scope given the constraints of resources. If we can specify the domain that a user belongs to more accurately with the heterogeneous information from social networks, and further maximize influence spread based on domains, traditional influence research from the general perspective as well as single perspective of structure or content will be hugely benefitted. In this paper we proposed a domain influence maximization model, which first divided users into different domains utilizing users social relations and weibo contents, and then maximized domain influence based on a greedy-dp hybrid algorithm. Experiments on Sina Weibo show that our model greatly improved the time cost of traditional models, with little errors.
关键词
影响力最大化 /
领域发现 /
领域影响力
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Key words
influence maximization /
domain discovery /
domain influence
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参考文献
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脚注
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基金
国家自然科学基金(61472006,91646202)
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