标题： 基于神经扩散模型的微观级联预测 显示英文标题

标题： Neural Diffusion Model for Microscopic Cascade Prediction

摘要： 信息扩散或级联的预测在过去十年中引起了广泛关注。 大多数级联预测工作旨在预测级联级别的宏观属性，例如级联的最终规模。 现有的微观级联预测模型专注于用户级别的建模，要么对用户如何被级联感染做出强烈假设，要么仅限于“谁感染了谁”信息明确标记的特定场景。 这些强假设简化了复杂的扩散机制，并阻碍了这些模型更好地拟合现实世界的级联数据。 此外，专注于特定场景的方法无法推广到扩散图未被观察到的一般情况。 为克服之前工作的缺点，我们提出了一个神经扩散模型（NDM），用于一般的微观级联预测。 NDM做出了较宽松的假设，并采用了包括注意力机制和卷积网络在内的深度学习技术进行级联建模。 这两方面的优势使我们的模型能够超越之前方法的限制，更好地拟合扩散数据并推广到未见过的级联。 在四个现实级联数据集上的扩散预测任务实验结果表明，我们的模型在F1分数方面相对于最佳基线可以实现高达26%的相对改进。 显示英文摘要

摘要： The prediction of information diffusion or cascade has attracted much attention over the last decade. Most cascade prediction works target on predicting cascade-level macroscopic properties such as the final size of a cascade. Existing microscopic cascade prediction models which focus on user-level modeling either make strong assumptions on how a user gets infected by a cascade or limit themselves to a specific scenario where "who infected whom" information is explicitly labeled. The strong assumptions oversimplify the complex diffusion mechanism and prevent these models from better fitting real-world cascade data. Also, the methods which focus on specific scenarios cannot be generalized to a general setting where the diffusion graph is unobserved. To overcome the drawbacks of previous works, we propose a Neural Diffusion Model (NDM) for general microscopic cascade prediction. NDM makes relaxed assumptions and employs deep learning techniques including attention mechanism and convolutional network for cascade modeling. Both advantages enable our model to go beyond the limitations of previous methods, better fit the diffusion data and generalize to unseen cascades. Experimental results on diffusion prediction task over four realistic cascade datasets show that our model can achieve a relative improvement up to 26% against the best performing baseline in terms of F1 score.