[KDD 2020] Neural Dynamics on Complex Networks
Aug 13, 202028 views
Learning continuous-time dynamics on complex networks is crucial for understanding, predicting and controlling complex systems,in science and engineering. However, this task is very challenging,due to the combinatorial complexities in the structures of high,dimensional systems, their elusive continuous-time nonlinear dynamics, and their structural-dynamic dependencies. To address,these challenges, we propose to combine Ordinary Differential,Equation Systems (ODEs) and Graph Neural Networks (GNNs) to,learn continuous-time dynamics on complex networks in a datadriven manner. We model differential equation systems by GNNs.,Instead of mapping through a discrete number of neural layers in,the forward process, we integrate GNN layers over continuous time,numerically, leading to capturing continuous-time dynamics on,graphs. Our model can be interpreted as a Continuous-time GNN,model or a Graph Neural ODEs model. Our model can be utilized for,continuous-time network dynamics prediction, structured sequence,prediction (a regularly-sampled case), and node semi-supervised,classification tasks (a one-snapshot case) in a unified framework.,We validate our model by extensive experiments in the above three,scenarios. The promising experimental results demonstrate our,model’s capability of jointly capturing the structure and dynamics,of complex systems in a unified framework.