Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs
A. Moallemy-Oureh. ACM/SIGAPP Symposium on Applied Computing (SAC), Seite 600--603. ACM, (2022)
The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques.
In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on.
While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs.
Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.
