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.
