CAREER: Embedding High-Order Interaction Events: Models, Algorithms, and Applications

从在线广告到商品推荐，从神经信号转导到基因调控，从疾病传播到国际事务，多个实体的高阶互动事件无处不在。例如，网购行为是顾客、产品和销售平台之间的互动事件。该项目为大规模高阶交互事件开发灵活、可解释和可扩展的贝叶斯嵌入，以了解事件之间的各种复杂关系并发现潜在的丰富模式。开发的工具可以从根本上促进许多重要的知识挖掘和预测任务。例如，预测危险在线交易的发生以加强金融安全、预测大流行疾病的爆发和传播以采取有效的预防措施、灾难的早期预警、研究谣言通过在线社交媒体传播的时间和方式等。目前的事件数据分析方法大多限于二元交互，并且存在粗糙、过度简化或不透明、无法解释的建模问题，计算效率有限。该项目的目标是开发贝叶斯嵌入，该嵌入可以高效地处理海量批量和快速流事件数据，捕捉实体的静态关系以及事件之间的各种短期、长期、触发、抑制和时变影响，并将所有这些编码到嵌入表示中，以揭示丰富的时间模式。研究将通过四个主要任务完成：(1)使用标记点过程设计高表达且透明的贝叶斯嵌入模型；(2)使用变分变换和合成蒙特卡罗近似来实现超大规模批处理数据上的随机小批次梯度和异步随机学习；(3)快速事件流的高效后验增量学习；(4)综合评估合成和真实世界的应用。此外，使用贝叶斯框架，开发的工具对噪声具有弹性，提供后验分布来量化不确定性，并将所有可能的结果整合到稳健的预测中。这一贡献预计将极大地促进将嵌入作为一种时间知识挖掘和预测分析的手段的使用。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

High-order interaction events of multiple entities are ubiquitous, ranging from online advertising to commodity recommendation, from neural-signal transduction to gene regulation to disease spreading to international affairs. For example, online shopping behaviors are interaction events between customers, products and selling platforms. This project develops flexible, interpretable, and scalable Bayesian embeddings for massive high-order interaction events, in order to understand a variety of complex relationships between the events and discover the underlying rich patterns. The developed tools can fundamentally promote many important knowledge mining and prediction tasks. Examples include predicting the occurrence of hazardous online transactions to enhance financial security, predicting the outbreak and spreading of pandemic diseases to take effective preventive actions, early warnings of catastrophes, studying when and how rumors propagate through online social media, etc. Current approaches for event data analysis are mostly restricted to binary interactions, and suffer from rough, over-simplified or opaque, uninterpretable modeling with limited computational efficiency. The goal of the project is to develop Bayesian embeddings that can efficiently process tremendous batch and fast streaming event data, capture both the static relationships of the entities and a variety of short-term, long-term, triggering, inhibition, and time varying influences among the events, and encode all of these into embedding representations to uncover rich temporal patterns. The research will be accomplished through four primary tasks: (1) using marked point processes to design highly expressive yet transparent Bayesian embedding models, (2) using variational transforms and composite Monte-Carlo approximations to fulfill stochastic mini-batch gradient and asynchronous stochastic learning on extremely large-scale batch data, (3) efficient posterior incremental learning for rapid event streams, and (4) comprehensive evaluations on synthetic and real-world applications. Moreover, using Bayesian frameworks, the developed tools are resilient to noises, provide posterior distributions to quantify uncertainties, and integrate all possible outcomes into robust predictions. The contribution is expected to dramatically promote the use of embedding as a means of temporal knowledge mining and predictive analytics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.