GraphRoute-Transfer, a graph-neural-network policy that assigns per-node timers from local structural features and is by construction permutation-and size-invariant, is proposed, a graph-neural-network policy that assigns per-node timers from local structural features and is by construction permutation-and size-invariant.
GraphRoute-Transfer, a graph-neural-network policy that assigns per-node timers from local structural features and is by construction permutation- and size-invariant, is proposed, a graph-neural-network policy that assigns per-node timers from local structural features and is by construction permutation- and size-invar...
Yuto Nakamura· Journal of Computing and Ele...· 0 citations
A reproducible discrete-time link-state convergence proxy with independent topology views, stochastic failure detection and link-state advertisement diffusion, shortest-path recomputation, and forwarding-loop/drop checks is implemented.
David Clarke, Mei Huang, Jonas Eriksen· International journal of inf...· 0 citations
The results support conformal shielding as a practical mechanism for exposing and controlling the safety-speed trade-off in learning-based routing, while also identifying the limits of guarantees under topology and load shift.
Fatima Rahman, Eric Nolan· Academic Journal of Applied...· 0 citations
Networks with highly dynamic data transmission demands and network topologies are common in real world. A fundamental problem in such networks is achieving scalable traffic allocation to maximize long-term total throughput under link capacity constraints. However, state-of-the-art (SOTA) works lack scalability. This is...
Zhaoxing Yang, Guiyun Fan, An-Jie Cao et al.· IEEE Transactions on Network...· 0 citations
This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.
Hao Sun, Fang He, Congyuan Ji et al.· arXiv.org· 0 citations
This work introduces TANGCO (Topology-Aware Neural Graph-Guided Capacity Optimization), which uses a graph neural network policy trained through the cascade simulator with policy-gradient learning and a heuristic anchor to allocate a fixed capacity budget across nodes to resist cascades under local load redistribution.
Orkun Irsoy, L. Akoglu, Osman Yağan· 0 citations
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