CAREER: SPARK: A Theoretical Framework for Discovering Complex Patterns in Big Attributed Networks

传感和计算技术的最新进展导致需要从科学、工程和商业等领域的异类信息源聚合大量数据，这些信息源自然以大属性网络的形式建模。大属性网络(BAN)的特征是(A)高维和异质网络拓扑和(B)高维和异质属性数据的组合。对BAN数据的有效分析依赖于同时进行的子图挖掘和特征选择，以发现感兴趣或有意义的复杂模式。然而，到目前为止，在这两个重要研究领域之间架起桥梁的工作还很少。因此，该项目的重点是统一范围广泛的复杂模式发现任务，例如，检测和预测社会事件(灾害、内乱)、异常模式(疾病爆发、网络攻击)、区别性子网络(癌症诊断)、知识模式(新知识建设)和故事情节(情报分析)，并解决与当今大数据时代无处不在的BAN数据相关的基本建模、算法和交互挑战。该项目将研究成果纳入跨学科课程的课程中，主题包括在研讨会、教程和研讨会上介绍的BAN数据中的复杂模式检测，并将包括推广活动，如纽约首都地区当地K-12教育的大数据分析夏令营。该项目的研究目标是：(1)开发一个统一的理论框架，用于在各种任务中发现BAN数据中的复杂模式；(2)使推理在由顶点、边和属性组成的超大型组合空间中易于计算；以及(3)使检测到的异质图案透明和可解释，并将异质用户反馈纳入检测过程中。该研究方法包括：(1)能够直接从BAN数据学习复杂感兴趣模式的新的原则性方法；(2)能够优化受不同子图拓扑和结构化稀疏模型约束的各种BAN特定模型目标的近线性时间公共推理算法；以及(3)能够建模和解释BAN数据中丰富的用户反馈方案的基于计算机可解释语言的系统。更多细节可以在：http://www.cs.albany.edu/~fchen/projects/BAN/.This奖项反映了国家科学基金会的法定使命，并已被认为值得支持，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。

Recent advances in sensing and computing techniques have led to a need for massive quantities of data to be aggregated from heterogeneous information sources in fields such as science, engineering, and business that are naturally modeled in the form of big attributed networks. A big attributed network (BAN) is characterized by a combination of (a) high-dimensional and heterogeneous network topologies and (b) high-dimensional and heterogeneous attribute data. Effective analysis of BAN data relies on simultaneous subgraph mining and feature selection for discovering complex patterns that are interesting or significant. However, as yet little has been done to bridge these two important research areas. The focus of this project is therefore to unify a wide range of complex pattern discovery tasks including, for example, the detection and forecasting of societal events (disasters, civil unrest), anomalous patterns (disease outbreaks, cyberattacks), discriminative subnetworks (cancer diagnosis), knowledge patterns (new knowledge building) and storylines (intelligence analysis), and to resolve the fundamental modeling, algorithmic, and interactive challenges associated with ubiquitous BAN data in today's big data era. This project incorporates the resulting research outcomes into the curricula of interdisciplinary courses on topics such as complex pattern detection in BAN data presented at seminars, tutorials, and workshops, and will include outreach activities such as a big data analytics summer camp for local K-12 education in New York's Capital Region.The research objectives of this project are: (1) the development of a unified, theoretical framework for discovering complex patterns in BAN data in various kinds of tasks; (2) making the inference computationally tractable in extremely large combined spaces composed of vertices, edges, and attributes; and (3) rendering the detected heterogeneous patterns transparent and interpretable and incorporating heterogeneous user feedback into the detection process. The research approach includes the development of: (1) novel principled methods capable of learning complex patterns of interest directly from BAN data; (2) near-linear-time common inference algorithms capable of optimizing a variety of BAN-specific model objectives that are subject to different constraints on subgraph topologies and structured sparsity models; and (3) a computer-interpretable language-based system capable of modeling and interpreting rich user feedback schemes in BAN data. More details can be found at: http://www.cs.albany.edu/~fchen/projects/BAN/.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.