  Formation mechanisms are fundamental to the study of complex networks, but
learning them from observations is challenging. In real-world domains, one
often has access only to the final constructed graph, instead of the full
construction process, and observed graphs exhibit complex structural
properties. In this work, we propose GraphOpt, an end-to-end framework that
jointly learns an implicit model of graph structure formation and discovers an
underlying optimization mechanism in the form of a latent objective function.
The learned objective can serve as an explanation for the observed graph
properties, thereby lending itself to transfer across different graphs within a
domain. GraphOpt poses link formation in graphs as a sequential decision-making
process and solves it using maximum entropy inverse reinforcement learning
algorithm. Further, it employs a novel continuous latent action space that aids
scalability. Empirically, we demonstrate that GraphOpt discovers a latent
objective transferable across graphs with different characteristics. GraphOpt
also learns a robust stochastic policy that achieves competitive link
prediction performance without being explicitly trained on this task and
further enables construction of graphs with properties similar to those of the
observed graph.
