  The interconnectedness and interdependence of modern graphs are growing ever
more complex, causing enormous resources for processing, storage,
communication, and decision-making of these graphs. In this work, we focus on
the task graph sparsification: an edge-reduced graph of a similar structure to
the original graph is produced while various user-defined graph metrics are
largely preserved. Existing graph sparsification methods are mostly
sampling-based, which introduce high computation complexity in general and lack
of flexibility for a different reduction objective. We present SparRL, the
first generic and effective graph sparsification framework enabled by deep
reinforcement learning. SparRL can easily adapt to different reduction goals
and promise graph-size-independent complexity. Extensive experiments show that
SparRL outperforms all prevailing sparsification methods in producing
high-quality sparsified graphs concerning a variety of objectives.
