Graph regression is a fundamental task that has gained significant attention in various graph learning tasks.

However, current methods are impeded by requiring long-time per-scene optimizations and cannot generalize to new scenes.

To address this challenge, we propose the framework Lambda for dangling detection and entity alignment.

We analytically characterize the convergence behavior of MTGC under general non-convex settings, overcoming challenges associated with couplings between correction terms.