Predicting the structure of dynamic graphs
Sevvandi Kandanaarachchi
Dynamic graph embeddings, inductive and incremental learning facilitate
predictive tasks such as node classification and link prediction. However,
predicting the structure of a graph at a future time step from a time series of
graphs, allowing for new nodes has not gained much attention. In this paper, we
present such an approach. We use time series methods to predict the node degree
at future time points and combine it with flux balance analysis -- a linear
programming method used in biochemistry -- to obtain the structure of future
graphs. Furthermore, we explore the predictive graph distribution for different
parameter values. We evaluate this method using synthetic and real datasets and
demonstrate its utility and applicability.