Influence Function Learning in Information Diffusion Networks
Influence Function Learning in Information Diffusion Networks
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发表时间：
2014-06
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通讯作者：
Nan Du;Yingyu Liang;Maria-Florina Balcan;Le Song
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作者：
Nan Du;Yingyu Liang;Maria-Florina Balcan;Le Song
我们能否从信息传播的级联中了解社交网络中一组人的影响力？这个问题通常通过两个阶段的方法来解决：首先学习扩散模型，然后基于学习的模型计算影响。因此，这种方法的成功在很大程度上依赖于扩散模型的正确性，这是很难验证的真实的世界数据。在本文中，我们利用的洞察力，在许多扩散模型中的影响函数是覆盖函数，并提出了一种新的参数化这些功能使用随机基函数的凸组合。此外，我们提出了一个有效的最大似然算法直接从级联数据学习这样的功能，从而绕过需要事先指定一个特定的扩散模型。我们为我们的方法提供了理论和实证分析，表明所提出的方法可以证明学习的影响函数与低样本复杂度，是强大的未知的扩散模型，并显着优于现有的方法在合成和真实的世界数据。
Can we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a two-stage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies heavily on the correctness of the diffusion model which is hard to verify for real world data. In this paper, we exploit the insight that the influence functions in many diffusion models are coverage functions, and propose a novel parameterization of such functions using a convex combination of random basis functions. Moreover, we propose an efficient maximum likelihood based algorithm to learn such functions directly from cascade data, and hence bypass the need to specify a particular diffusion model in advance. We provide both theoretical and empirical analysis for our approach, showing that the proposed approach can provably learn the influence function with low sample complexity, be robust to the unknown diffusion models, and significantly outperform existing approaches in both synthetic and real world data.