Modeling Information Diffusion in Implicit Networks
Modeling Information Diffusion in Implicit Networks
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DOI：
10.1109/icdm.2010.22
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
Jaewon Yang;J. Leskovec
中科院分区：
文献类型：
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作者：
Jaewon Yang;J. Leskovec
社交媒体是制作和传播实时信息的核心领域。尽管这种信息流动传统上被认为是社交网络上的传播过程，但其背后的现象是众多参与者之间复杂的互动网络的结果。在这里，我们开发了线性影响模型，而不是需要社会网络的知识，然后通过预测哪个节点将影响网络中的其他节点来建模扩散，我们专注于建模节点对通过（隐式）网络扩散速率的全局影响。我们将新感染节点的数量建模为其他节点在过去被感染的函数。对于每个节点，我们估计一个影响函数，该函数量化了随着时间的推移，有多少后续感染可以归因于该节点的影响。模型的非参数公式化导致了一个简单的最小二乘问题，可以在大型数据集上解决。我们在一组5亿条推文和一组1.7亿条新闻文章和博客文章上验证了我们的模型。我们表明，线性影响模型准确地模型节点的影响，可靠地预测信息扩散的时间动态。我们发现，影响的个人参与者的模式显着不同，这取决于节点的类型和信息的主题。
Social media forms a central domain for the production and dissemination of real-time information. Even though such flows of information have traditionally been thought of as diffusion processes over social networks, the underlying phenomena are the result of a complex web of interactions among numerous participants. Here we develop the Linear Influence Model where rather than requiring the knowledge of the social network and then modeling the diffusion by predicting which node will influence which other nodes in the network, we focus on modeling the global influence of a node on the rate of diffusion through the (implicit) network. We model the number of newly infected nodes as a function of which other nodes got infected in the past. For each node we estimate an influence function that quantifies how many subsequent infections can be attributed to the influence of that node over time. A nonparametric formulation of the model leads to a simple least squares problem that can be solved on large datasets. We validate our model on a set of 500 million tweets and a set of 170 million news articles and blog posts. We show that the Linear Influence Model accurately models influences of nodes and reliably predicts the temporal dynamics of information diffusion. We find that patterns of influence of individual participants differ significantly depending on the type of the node and the topic of the information.