GraphRoute-Transfer: Topology-Generalizable Routing Convergence Optimization via Graph Reinforcement Learning

Abstract

Fast and stable routing convergence is critical in large IP networks, and the interior-gateway-protocol (IGP) timers that govern failure detection (Hello/Dead intervals) expose a fundamental tension: aggressive timers detect failures quickly but inflate control overhead and trigger route flaps, whereas conservative timers are stable but slow. Recent work such as DRL-Adapt has shown that deep reinforcement learning can tune these timers better than static defaults, but it operates on a flat, globally-aggregated network state and emits a single network-wide timer, so it can neither exploit the spatial heterogeneity of real topologies nor transfer architecturally across networks of different size. We propose GraphRoute-Transfer, a graph-neural-network policy that assigns per-node timers from local structural features and is by construction permutation- and size-invariant. Because control-plane fragility and failure criticality are spatially heterogeneous, the cost-minimizing timer assignment varies across the graph; our policy learns this mapping and applies it zero-shot to unseen topologies of arbitrary size. Training is guided by a coordinate-descent search oracle on a convergence-cost objective, so the expensive per-topology optimization is amortized into a sub-millisecond inference. On 231 real topologies from the Internet Topology Zoo, GraphRoute-Transfer reduces mean convergence time by 37.3% relative to the OSPF default and to a flat DRL baseline, lowers the composite convergence-cost objective by 16.4% over the flat baseline, and attains 1.317 cost—within 0.3% of the search oracle—while running about 8,160× faster than the search. Crucially, a policy trained only on networks with ≤70 nodes maintains its gains on unseen networks up to 140 nodes, whereas the flat baseline degenerates to a global constant that cannot adapt.