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，工程和业务等领域的异质信息源中汇总大量数据，这些信息源自然而然地以大型归因网络的形式进行建模。一个大的归因网络（BAN）的特征是（a）高维和异质网络拓扑结合，以及（b）高维和异质属性数据。对禁令数据的有效分析依赖于同时的子图挖掘和特征选择，以发现有趣或重要的复杂模式。但是，尚未完成桥接这两个重要的研究领域。 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,以及与当今大数据时代无处不在的禁令数据相关的互动挑战。该项目将结果的结果纳入了有关主题的跨学科课程的课程，例如在研讨会，教程和研讨会上呈现的禁令数据中的复杂模式检测，并将包括诸如纽约资本区域中K-12教育的本地K-12教育的大型数据分析夏令营之类的外展活动。多种任务； （2）在由顶点，边缘和属性组成的极大组合空间中进行推理计算典型； （3）将检测到的异质模式透明且可解释，并将异质用户反馈纳入检测过程。研究方法包括：（1）直接从禁令数据中学习复杂的感兴趣模式的新型原则方法； （2）能够优化各种禁令特异性模型目标的近线性通用推理算法，这些目标受到子图拓扑和结构化稀疏模型的不同限制； （3）一个基于计算机介入的基于语言的系统，能够在禁令数据中建模和解释丰富的用户反馈方案。可以在以下网址找到更多详细信息：http：//www.cs.albany.edu/~fchen/projects/ban/.this Award反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响审查标准来评估的支持。

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