Proportionally fair (PF) controller placement, introduced in [1] and formalised in [2] with Logistic Regression and Random Forest estimators of the pairwise coverage matrix Π on topological features, and extended in [3] to agricultural sensor coverage, is the framework this paper evaluates. This paper stress-tests that pipeline: we replace the topological-feature classifiers with a graph-native estimator (a two-layer GraphSAGE encoder with a pairwise readout) and probability-profile clustering with embedding-space clustering, keeping the linearised PF objective, the partition-matroid constraint, and the heap-based selection rule unchanged. The four resulting estimators are evaluated on Barabási-Albert topologies of 75, 300, and 500 nodes along four axes: worst-case placement quality, training wall-clock time, expected calibration error, and interpretability. Logistic Regression delivers the highest worst-case coverage at moderate-to-heavy attack intensities (run-mean margins up to 3.8 functional nodes at 75 and 20.0 at 300), trains roughly 24× faster than GraphSAGE at 500 nodes, gives raw probabilities better calibrated than the deep estimator (ECE 0.064 versus 0.237, both ≈ 0.07 after isotonic regression), and remains directly inspectable through its per-feature coefficients. GraphSAGE surpasses neither supervised baseline on any axis. The result is qualified and scale-dependent. One component of the deep pipeline does produce a measurable gain: embedding-space K-means surpasses probability-space K-means by 19.34 functional nodes at 300 and 4.33 at 500 while trailing by 1.40 at 75. Within the empirical scope of this study (synthetic Barabási-Albert topologies of 75, 300, and 500 nodes; failure model with 12% targeted removals of high-betweenness nodes plus 88% uniform random removals), the recommended deployment configuration retains Logistic Regression as the production estimator of Π and confines the deep encoder to the clustering step on topologies above the regime-transition size. Generalisation to operational SDN backbones, to graphs of substantially different degree distribution, or to adaptive-adversary attack models is outside the empirical scope and is listed as a limitation in Section 8.
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