Based on our definitions, we identify and analyze six real-world benchmarks spanning from homophilic to heterophilic link prediction settings, with graphs containing up to 30M edges.
To solve this optimization, we propose a simulation-free training objective with a model parameterization that imposes the desired boundary conditions by design.