Mining topic-level influence in heterogeneous networks
Mining topic-level influence in heterogeneous networks
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DOI：
10.1145/1871437.1871467
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Lu Liu;Jie Tang;Jiawei Han;Meng Jiang;Shiqiang Yang
中科院分区：
文献类型：
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作者：
Lu Liu;Jie Tang;Jiawei Han;Meng Jiang;Shiqiang Yang
影响力是一种复杂而微妙的力量，控制着社交网络的动态以及相关用户的行为。了解影响力可以使各种应用受益，例如病毒式营销、推荐和信息检索。然而，大多数现有的社会影响力分析工作都集中在验证社会影响力的存在。很少有工作系统地研究如何挖掘异构网络中节点之间直接和间接影响的强度。为了解决这个问题，我们提出了一种生成图模型，它利用异构链接信息和与网络中每个节点相关的文本内容来挖掘主题级的直接影响。基于学习到的直接影响力，提出了主题级影响力传播和聚合算法来推导节点之间的间接影响力。我们进一步研究发现的主题级影响如何帮助预测用户行为。我们在三种不同类型的数据集上验证了该方法：Twitter、Digg 和引文网络。定性地，我们的方法可以发现异构网络中有趣的影响模式。定量地讲，学习到的主题级影响力可以大大提高用户行为预测的准确性。
Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. Understanding influence can benefit various applications such as viral marketing, recommendation, and information retrieval. However, most existing works on social influence analysis have focused on verifying the existence of social influence. Few works systematically investigate how to mine the strength of direct and indirect influence between nodes in heterogeneous networks. To address the problem, we propose a generative graphical model which utilizes the heterogeneous link information and the textual content associated with each node in the network to mine topic-level direct influence. Based on the learned direct influence, a topic-level influence propagation and aggregation algorithm is proposed to derive the indirect influence between nodes. We further study how the discovered topic-level influence can help the prediction of user behaviors. We validate the approach on three different genres of data sets: Twitter, Digg, and citation networks. Qualitatively, our approach can discover interesting influence patterns in heterogeneous networks. Quantitatively, the learned topic-level influence can greatly improve the accuracy of user behavior prediction.