III: Small: Explaining heterogeneity within and across evolving networks

III：小：解释不断发展的网络内部和之间的异质性

• 批准号: 1817046
• 负责人: Ambuj Singh
• 金额: $ 50万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817046&HistoricalAwards=false
• 关键词: III; Small; Explaining; heterogeneity; within

This project will develop novel methods for analyzing and modeling heterogeneous dynamic networked data. Network data arises in a number of application domains ranging from Internet of Things, cloud computing, software analysis, neuroscience, biology, geography, to social sciences. Accordingly, network analysis has emerged as a major paradigm for exploring complex processes behind observed data. Compared to high dimensional data, analysis of network data is more challenging due to interdependencies between entities, the presence of attributes, and the natural evolution of networks over time. The goal of the project will be to understand and model the heterogeneity of behaviors in dynamic networks. The project will have a transformative impact on big data problems that are enabled by a network-centric approach to exploiting dynamic, heterogeneous data, such as brain networks. The project will integrate research and education by introducing methods and results of the project into courses and seminars, and train a diverse group of undergraduate and graduate students.The project's focus will be on heterogeneity in dynamic networks: heterogeneity of node behaviors across network structure and time, heterogeneity of the coupling of structure and attributes, and heterogeneity across networks. Against this backdrop, the project will consider the basic problems of clustering (partitioning), classification/regression, decomposition of networks into its basis elements, and the problem of explaining global network behaviors by small network fragments. These problems will be considered for a single network and for multiple networks. Within a network, heterogeneity is observed when nodes or clusters exhibit different behaviors, for instance due to hidden or missing data. Across networks, heterogeneity is observed in the diversity of subject populations or among network instances. The first research thrust will apply spectral theory for partitioning attributed and dynamic networks. The second research thrust will apply convex optimization to find clusters while tolerating heterogeneity across network structure and time. It will also develop methods for estimating graphical models for multiple dynamic networks. The final research thrust will focus on the discovery of succinct sub-networks that are predictive and that evolve concurrently with the underlying networks.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.

本项目将开发分析和建模异构动态网络数据的新方法。网络数据出现在从物联网、云计算、软件分析、神经科学、生物学、地理学到社会科学的许多应用领域中。因此，网络分析已成为探索观察数据背后复杂过程的主要范式。与高维数据相比，网络数据的分析更具挑战性，这是由于实体之间的相互依赖性，属性的存在以及网络随时间的自然演变。该项目的目标是理解和建模动态网络中行为的异质性。该项目将对大数据问题产生变革性的影响，这些问题是通过以网络为中心的方法来利用动态的异构数据，如大脑网络。本项目将通过在课程和研讨会中介绍本项目的方法和成果，实现研究和教育的一体化，培养多样化的本科生和研究生。本项目的研究重点是动态网络中的异质性：网络结构和时间上的节点行为的异质性、结构和属性的耦合的异质性、网络间的异质性。在此背景下，该项目将考虑聚类（分区），分类/回归，网络分解为基本元素的基本问题，以及通过小网络片段解释全球网络行为的问题。这些问题将被认为是一个单一的网络和多个网络。在网络中，当节点或集群表现出不同的行为时，例如由于隐藏或丢失数据，就会观察到异质性。在整个网络中，异质性被观察到的主题人群或网络实例的多样性。第一个研究重点将应用谱理论划分属性和动态网络。第二个研究重点将应用凸优化来寻找集群，同时容忍网络结构和时间上的异构性。它还将开发用于估计多个动态网络的图形模型的方法。最终的研究重点将集中在发现简洁的子网络，这些子网络具有预测性，并与底层网络同步发展。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Deep Representations for Time-varying Brain Datasets

• DOI: 10.1145/3534678.3539301
• 发表时间: 2022-05
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Sikun Lin;Shuyun Tang;Scott T. Grafton;Ambuj K. Singh

A Broader Picture of Random-walk Based Graph Embedding

• DOI: 10.1145/3447548.3467300
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Zexi Huang;A. Silva;Ambuj K. Singh

Combining Physics and Machine Learning for Network Flow Estimation

• 发表时间: 2021
• 作者: A. Silva;Furkan Kocayusufoglu;Saber Jafarpour;F. Bullo;A. Swami;Ambuj K. Singh

Tree++: Truncated Tree Based Graph Kernels

• DOI: 10.1109/tkde.2019.2946149
• 发表时间: 2020-02
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Wei Ye;Zhen Wang;Rachel Redberg;Ambuj K. Singh

Incorporating User's Preference into Attributed Graph Clustering

• DOI: 10.1109/tkde.2020.2976063
• 发表时间: 2020-03
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Wei Ye;Dominik Mautz;Christian Boehm;Ambuj K. Singh;C. Plant