GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs

There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused on obtaining high-quality solutions, scalability to billion-sized graphs has not been adequately addressed. In addition, the impact of budgetconstraint, which is necessary for many practical scenarios, remains to be studied.