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

职业：SPARK：发现大属性网络中复杂模式的理论框架

• 批准号: 1750911
• 负责人: Feng Chen
• 金额: $ 53.7万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750911&HistoricalAwards=false
• 关键词: CAREER; SPARK; Theoretical; Framework; Discovering

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.

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构信息源聚合大量数据，这些数据以大属性网络的形式自然建模。大属性网络（BAN）的特点是（a）高维异构网络拓扑和（b）高维异构属性数据的组合。 BAN 数据的有效分析依赖于同步子图挖掘和特征选择，以发现有趣或重要的复杂模式。然而，迄今为止，在连接这两个重要的研究领域方面还没有采取什么措施。因此，该项目的重点是统一广泛的复杂模式发现任务，包括例如社会事件（灾难、内乱）、异常模式（疾病爆发、网络攻击）、判别子网络（癌症诊断）的检测和预测）、知识模式（新知识构建）和故事情节（情报分析），并解决与无处不在的 BAN 数据相关的基本建模、算法和交互挑战当今的大数据时代。该项目将由此产生的研究成果纳入跨学科课程的课程中，主题包括在研讨会、教程和讲习班上介绍的 BAN 数据中的复杂模式检测等主题，并将包括外展活动，例如针对当地 K-12 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，用于发现各种任务中 BAN 数据中的复杂模式； (2) 在由顶点、边和属性组成的极大组合空间中使推理在计算上易于处理； (3)使检测到的异构模式透明且可解释，并将异构用户反馈纳入检测过程。研究方法包括开发：（1）能够直接从 BAN 数据学习复杂的感兴趣模式的新颖原理方法； (2) 近线性时间通用推理算法，能够优化各种特定于 BAN 的模型目标，这些目标受到子图拓扑和结构化稀疏模型的不同约束； (3) 一个基于计算机可解释语言的系统，能够建模和解释 BAN 数据中丰富的用户反馈方案。更多详细信息，请访问：http://www.cs.albany.edu/~fchen/projects/BAN/。该奖项反映了 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进行评估，被认为值得支持审查标准。

项目成果

Graph Anomaly Detection Based on Steiner Connectivity and Density

• DOI: 10.1109/jproc.2018.2813311
• 发表时间: 2018-04
• 期刊: Proceedings of the IEEE
• 影响因子: 20.6
• 作者: Jose Cadena;F. Chen;A. Vullikanti

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning

• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: Krishnateja Killamsetty;Xujiang Zhao;F. Chen;Rishabh K. Iyer

Evading provenance-based ML detectors with adversarial system actions

通过对抗性系统操作规避基于来源的机器学习检测器

• 发表时间: 2023
• 期刊: SEC '23: Proceedings of the 32nd USENIX Conference on Security Symposium
• 作者: Mukherjee, Kunal;Wiedemeier, Joshua;Wang, Tianhao;Wei, James;Chen, Feng;Kim, Muhyun;Kantarcioglu, Murat;Jee, Kangkook
• 通讯作者: Jee, Kangkook

Detecting Media Self-Censorship without Explicit Training Data

在没有显式训练数据的情况下检测媒体自我审查

• DOI: 10.1137/1.9781611976236.62
• 发表时间: 2020
• 期刊: Proceedings of the 2020 SIAM International Conference on Data Mining
• 作者: Tao, Rongrong;Zhou, Baojian;Chen, Feng;Mares, Dvid;Butler, Patrick;Ramakrishnan, Naren;Kennedy, Ryan
• 通讯作者: Kennedy, Ryan

Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization

• DOI: 10.48550/arxiv.2311.05795
• 发表时间: 2023-11
• 期刊: ArXiv
• 作者: Russell Hart;Linlin Yu;Yifei Lou;Feng Chen