Learning from A Single Graph is All You Need for Near-Shortest Path
  Routing in Wireless Networks
Yung-Fu Chen Sen Lin Anish Arora
We propose a learning algorithm for local routing policies that needs only a
few data samples obtained from a single graph while generalizing to all random
graphs in a standard model of wireless networks. We thus solve the all-pairs
near-shortest path problem by training deep neural networks (DNNs) that
efficiently and scalably learn routing policies that are local, i.e., they only
consider node states and the states of neighboring nodes. Remarkably, one of
these DNNs we train learns a policy that exactly matches the performance of
greedy forwarding; another generally outperforms greedy forwarding. Our
algorithm design exploits network domain knowledge in several ways: First, in
the selection of input features and, second, in the selection of a ``seed
graph'' and subsamples from its shortest paths. The leverage of domain
knowledge provides theoretical explainability of why the seed graph and node
subsampling suffice for learning that is efficient, scalable, and
generalizable. Simulation-based results on uniform random graphs with diverse
sizes and densities empirically corroborate that using samples generated from a
few routing paths in a modest-sized seed graph quickly learns a model that is
generalizable across (almost) all random graphs in the wireless network model.