标题： 关于在线社交网络中信息扩散的扩散逻辑模型预测 显示英文标题

标题： Diffusive Logistic Model Towards Predicting Information Diffusion in Online Social Networks

摘要： 在线社交网络近年来已成为在数亿终端用户之间传播信息和影响的有效且创新的渠道。许多先前的工作进行了实证研究并提出了扩散模型，以理解在线社交网络中的信息扩散过程。然而，大多数这些研究集中在时间维度上的信息扩散，即信息如何随时间传播。很少有尝试去理解时间和空间两个维度上的信息扩散。 本文提出了一种偏微分方程（PDE），具体来说是一种扩散逻辑斯蒂（DL）方程，用于建模在线社交网络中信息扩散的时间和空间特性。更具体地说，我们开发了一个基于PDE的理论框架，以测量和预测在一段时间后，距离原始信息源给定距离内的受影响用户的密度。随着时间推移和距离变化的受影响用户密度提供了关于实际信息扩散过程的有价值见解。 我们在从Digg社交新闻网站收集的真实数据集中展示了时间与空间模式，并验证了提出的DL方程在预测信息扩散过程方面的有效性。我们的实验结果显示，DL模型确实能够描述和预测在线社交网络中的信息传播过程。例如，在Digg上最受欢迎的拥有24,099票的新闻中，DL模型在前6小时所有距离上的平均预测准确率为92.08%。 据我们所知，这是首次尝试使用基于PDE的模型来研究在线社交网络中时间和空间维度上的信息扩散过程。 显示英文摘要

摘要： Online social networks have recently become an effective and innovative channel for spreading information and influence among hundreds of millions of end users. Many prior work have carried out empirical studies and proposed diffusion models to understand the information diffusion process in online social networks. However, most of these studies focus on the information diffusion in temporal dimension, that is, how the information propagates over time. Little attempt has been given on understanding information diffusion over both temporal and spatial dimensions. In this paper, we propose a Partial Differential Equation (PDE), specifically, a Diffusive Logistic (DL) equation to model the temporal and spatial characteristics of information diffusion in online social networks. To be more specific, we develop a PDE-based theoretical framework to measure and predict the density of influenced users at a given distance from the original information source after a time period. The density of influenced users over time and distance provides valuable insight on the actual information diffusion process. We present the temporal and spatial patterns in a real dataset collected from Digg social news site, and validate the proposed DL equation in terms of predicting the information diffusion process. Our experiment results show that the DL model is indeed able to characterize and predict the process of information propagation in online social networks. For example, for the most popular news with 24,099 votes in Digg, the average prediction accuracy of DL model over all distances during the first 6 hours is 92.08%. To the best of our knowledge, this paper is the first attempt to use PDE-based model to study the information diffusion process in both temporal and spatial dimensions in online social networks.