Tractable Models for Information Diffusion in Social Networks
Tractable Models for Information Diffusion in Social Networks
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DOI：
10.1007/11871637_27
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发表时间：
2006-09
期刊：
影响因子：
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通讯作者：
M. Kimura;Kazumi Saito
中科院分区：
文献类型：
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作者：
M. Kimura;Kazumi Saito
在基于独立级联模型（ICM）的大规模社会网络中寻找对信息扩散有影响的节点时，需要计算一组给定节点所影响的节点的期望数量。然而，一个很好的估计这个数量需要大量的计算在ICM。在本文中，我们提出了两个自然的特殊情况下的ICM，这样一个很好的估计，这个数量可以有效地计算。使用真实的大规模社交网络，我们的实验表明，提取有影响力的节点，所提出的模型可以提供新的排名方法，不同的ICM，典型的社会网络分析方法，和“PageRank”方法。此外，我们的实验表明，当通过链接的传播概率很小，他们可以很好的近似ICM找到有影响力的节点集。
When we consider the problem of finding influential nodes for information diffusion in a large-scale social network based on theIndependent Cascade Model (ICM), we need to compute the expected number of nodes influenced by a given set of nodes. However, a good estimate of this quantity needs a large amount of computation in the ICM. In this paper, we propose two natural special cases of the ICM such that a good estimate of this quantity can be efficiently computed. Using real large-scale social networks, we experimentally demonstrate that for extracting influential nodes, the proposed models can provide novel ranking methods that are different from the ICM, typical methods of social network analysis, and “PageRank” method. Moreover, we experimentally demonstrate that when the propagation probabilities through links are small, they can give good approximations to the ICM for finding sets of influential nodes.