  Data-driven machine learning approaches have recently been proposed to
facilitate wireless network optimization by learning latent knowledge from
historical optimization instances. However, existing methods do not well handle
the topology information that directly impacts the network optimization
results. Directly operating on simple representations, e.g., adjacency
matrices, results in poor generalization performance as the learned results
depend on specific ordering of the network elements in the training data. To
address this issue, we propose a two-stage topology-aware machine learning
framework (TALF), which trains a graph embedding unit and a deep feed-forward
network (DFN) jointly. By propagating and summarizing the underlying graph
topological information, TALF encodes the topology in the vector representation
of the optimization instance, which is used by the later DFN to infer critical
structures of an optimal or near-optimal solution. The proposed approach is
evaluated on a canonical wireless network flow problem with diverse network
typologies and flow deployments. In-depth study on trade-off between efficiency
and effectiveness of the inference results is also conducted, and we show that
our approach is better at differentiate links by saving up to 60% computation
time at over 90% solution quality.
