Graph Neural Lasso for Dynamic Network Regression

In this paper, we will study the dynamic network regression problem, which focuses on inferring both individual entities' changing attribute values and the dynamic relationships among the entities in the network data simultaneously. To resolve the problem, a novel graph neural network, namely graph neural lasso (GNL), will be proposed in this paper. To model the real-time changes of nodes in the network, GNL extends gated diffusive unit (GDU) to the regression scenario and uses it as the basic neuron unit. GNL can effectively model the dynamic relationships among the nodes based on an attention mechanism.