III: Small: Deep Generative Models for Temporal Graph Generation and Interpretation

III：小：用于时间图生成和解释的深度生成模型

• 批准号: 2007716
• 负责人: Liang Zhao
• 金额: $ 49.81万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007716&HistoricalAwards=false
• 关键词: III; Small; Deep; Generative; Models

Temporal graphs represent a crucial type of data structure where the entities and their connections evolve over time. These time-evolving phenomena are ubiquitous in real-world networks such as social networks, biological networks, and cyber networks. Existing generative models of temporal graphs typically rely on the principles of temporal process of network generation predefined by human heuristics and prior knowledge, such as temporal exponential random graphs, randomized reference models, and dynamic Bayesian models. They usually fit well towards the properties that the predefined principles are tailored for, but usually cannot do well for the others. Unfortunately, the mechanisms of many critical real-world network dynamics are still largely unknown, such as co-evolution of structural and functional connectivities in brain networks, catastrophic cascading failures in power networks, and malware epidemics in the Internet of Things. This project focuses on developing a transformative framework for temporal graphs generative modeling that can automatically learn, characterize, and interpret the underlying patterns and principles from temporal graph observation data. It aims at significantly benefiting the related scientific and engineering domains with open-sourced tools for temporal graphs modeling and network dynamics knowledge distillation. The project includes educational and engagement activities that will substantially increase the community's understanding of temporal graphs.This project will develop a generic framework of generative deep neural networks for temporal graph modeling, generation, and interpretation. The two major types of network dynamic patterns will be investigated, including "topological dynamics of a graph" (e.g., growth of a social network) and "activity dynamics on a graph" (e.g., real-time communications in contact networks). The proposed framework will: 1) automatically learn the (unknown) process of topological dynamics and activity dynamics in discrete- and continuous-time temporal graphs, 2) enforce validity constraints on dynamic topologies and time-evolving activities over the generated temporal graphs, and 3) pursue the temporal graph model interpretability for network dynamic patterns distillation and model intervention. To achieve the above research goals, a number of research activities will be conducted including: i) develop scalable deep generative models for dynamic topologies of large temporal graphs under the temporal-topological constraints, ii) propose novel deep generative models for activity dynamics in temporal graphs under validity-guarantee activation functions, iii) design strategies for modeling the co-evolution of topological dynamics and activity dynamics, iv) pursue model interpretability enhancement by disentangling static and dynamic patterns as well as post-hoc interpretation on the generated temporal graphs by dynamic graph attention and dynamic subgraph detection techniques, and v) develop a novel human-model interaction system for temporal graph visualization and model intervention.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.

时间图代表了一个至关重要的数据结构类型，其中实体及其连接会随着时间的流逝而发展。这些随着时间的推移现象在社交网络，生物网络和网络网络等现实世界网络中无处不在。现有的时间图的生成模型通常依赖于由人类启发式和先验知识预定的网络生成的时间过程的原理，例如时间指数随机图，随机参考模型和动态贝叶斯模型。它们通常非常适合为预定义的原则量身定制的特性，但通常对其他原则做得不好。不幸的是，许多关键现实世界网络动力学的机制仍然很大程度上是未知的，例如大脑网络中结构和功能连接的共同发展，电力网络中的灾难性级联失败以及物联网中的恶意软件流行病。该项目着重于为时间图生成建模开发一个变革性框架，该框架可以自动学习，表征和解释时间图观察数据中的基本模式和原理。它的目的是通过开源图形建模和网络动态知识蒸馏来显着使相关的科学和工程领域受益。该项目包括教育和参与活动，这些活动将大大提高社区对时间表的理解。该项目将开发一个通用的生成深层神经网络的框架，用于时间图形建模，生成和解释。将研究两种主要类型的网络动态模式，包括“图形的拓扑动态”（例如，社交网络的增长）和“图上的活动动态”（例如，接触网络中的实时通信）。拟议的框架将：1）自动学习离散和连续时间时间图中拓扑动态和活动动态的过程（未知）过程，2）在生成的时间图上对动态拓扑和时间不断发展的活动的有效性约束，以及3）进行时间图表模型，以使网络模型解释性解释性动态模式模型差异化。为了实现上述研究目标，将进行许多研究活动，包括：i）在时间流行限制下开发可扩展的深层生成模型，用于大型时间图的动态拓扑，ii）提出新型的深层生成模型，以在有效性 - 保证激活功能下，在造型的模型中，在造型动态和活动模型下，在造型中，在时间图中进行活性动态，以建模，以建模造型动态。通过动态图表和动态子图检测技术进行解开静态和动态模式以及对生成的时间图的事后解释，以及v）v）开发了一种新型的人类模型相互作用系统，用于时间图形可视化和模型干预。该奖项反映了NSF的法定任务，并通过评估范围的范围来反映出支持者的知识范围，并已被评估范围。

项目成果

BEAN: Interpretable and Efficient Learning With Biologically-Enhanced Artificial Neuronal Assembly Regularization.

• DOI: 10.3389/fnbot.2021.567482
• 发表时间: 2021
• 期刊: Frontiers in neurorobotics
• 影响因子: 3.1
• 作者: Gao Y;Ascoli GA;Zhao L
• 通讯作者: Zhao L

CPM: A general feature dependency pattern mining framework for contrast multivariate time series

CPM：用于对比多元时间序列的通用特征依赖模式挖掘框架

• DOI: 10.1016/j.patcog.2020.107711
• 发表时间: 2021
• 期刊: Pattern Recognition
• 影响因子: 8
• 作者: Li, Qingzhe;Zhao, Liang;Lee, Yi-Ching;Sassan, Avesta;Lin, Jessica
• 通讯作者: Lin, Jessica

RES: A Robust Framework for Guiding Visual Explanation

• DOI: 10.1145/3534678.3539419
• 发表时间: 2022-06
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Yuyang Gao;Tong Sun;Guangji Bai;Siyi Gu;S. Hong;Liang Zhao

A Systematic Survey on Deep Generative Models for Graph Generation

• DOI: 10.1109/tpami.2022.3214832
• 发表时间: 2023-05-01
• 期刊: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
• 影响因子: 23.6
• 作者: Guo, Xiaojie;Zhao, Liang
• 通讯作者: Zhao, Liang

"Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks."

“具有漂移感知动态神经网络的时域泛化。”

• 发表时间: 2023
• 期刊: In The Eleventh International Conference on Learning Representations. 2022.
• 作者: Bai, Guangji;Chen Ling;Liang Zhao.
• 通讯作者: Liang Zhao.