Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network
Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network
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DOI：
10.1007/978-3-642-12079-4_20
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Kazumi Saito;M. Kimura;K. Ohara;H. Motoda
中科院分区：
文献类型：
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作者：
Kazumi Saito;M. Kimura;K. Ohara;H. Motoda
我们研究如何以及不同的信息扩散模型解释观察数据，通过学习他们的参数和进行行为分析。我们使用两个模型（CTIC，CTLT），包括连续时间延迟，是众所周知的独立级联（IC）和线性阈值（LT）模型的扩展。本文首先研究了CTLT模型的参数学习问题，并将其应用于两类任务：有影响力节点的排序和话题传播的行为分析，并将结果与CTIC模型和不考虑扩散现象的传统算法进行了比较。我们表明，使用模型和排名精度是非常重要的，但来自学习的参数值的主题的传播速度是相当不敏感的模型使用的模型。
We investigate how well different information diffusion models explain observation data by learning their parameters and performing behavioral analyses. We use two models (CTIC, CTLT) that incorporate continuous time delay and are extension of well known Independent Cascade (IC) and Linear Threshold (LT) models. We first focus on parameter learning of CTLT model that is not known so far, and apply it to two kinds of tasks: ranking influential nodes and behavioral analysis of topic propagation, and compare the results with CTIC model together with conventional heuristics that do not consider diffusion phenomena. We show that it is important to use models and the ranking accuracy is highly sensitive to the model used but the propagation speed of topics that are derived from the learned parameter values is rather insensitive to the model used.