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

时态图代表了一种重要的数据结构类型，其中实体及其连接随着时间的推移而演变。这些随时间演化的现象普遍存在于社会网络、生物网络和网络等现实世界的网络中。现有的时态图生成模型通常依赖于由人类启发式和先验知识预先定义的时态网络生成过程的原理，如时态指数随机图、随机参考模型和动态贝叶斯模型。它们通常适合为预定义原则量身定做的属性，但通常不能很好地适用于其他属性。不幸的是，许多关键的现实世界网络动力学的机制在很大程度上仍然未知，例如大脑网络中结构和功能连接性的共同进化，电力网络中的灾难性连锁故障，以及物联网中的恶意软件流行。该项目致力于开发一个用于时态图形生成性建模的变革性框架，该框架可以自动学习、表征和解释来自时态图形观测数据的潜在模式和原理。它旨在通过开放源码的时态图建模和网络动力学知识提取工具，为相关科学和工程领域带来显着的好处。该项目包括教育和参与活动，将大大增加社区对时间图的理解。该项目将开发一个通用的生成性深度神经网络框架，用于时间图的建模、生成和解释。将研究两种主要类型的网络动态模式，包括“图的拓扑动态”(例如，社交网络的增长)和“图上的活动动态”(例如，联系网络中的实时通信)。该框架将：1)自动学习离散和连续时间时态图中(未知)的拓扑动力学和活动动力学过程；2)在生成的时态图上对动态拓扑和时间演化活动实施有效性约束；3)追求时态图模型的可解释性，用于网络动态模式提取和模型干预。为了实现上述研究目标，将开展一系列研究活动，包括：i)在时间-拓扑约束下开发可扩展的大型时态图动态拓扑的深度生成模型；ii)在有效性保证激活函数下提出新的时态图活动动态的深度生成模型；iii)设计拓扑动力学和活动动力学共同演化的建模策略；iv)通过分离静态和动态模式以及通过动态图注意和动态子图检测技术对生成的时态图进行事后解释来增强模型的可解释性，以及v)开发一种用于时间图可视化和模型干预的新型人-模型交互系统。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审查标准进行评估，被认为值得支持。

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.