Graph ML & Graph Neural Networks
From PageRank features to GraphSAGE and PinSage — and when a graph feature beats a GNN
Learn when and how to learn on graphs and defend each choice in an interview: the three task types (node, link, graph level) and the homophily check, with the trap that edge homophily of 0.920 on a graph with 5 percent fraud is barely above the 0.905 expected by chance, computed on a synthetic transaction graph; classic graph features (degree, PageRank, triangle and clustering counts, Louvain community size and density) fed to a gradient-boosted model, which in the worked example lifted time-split PR-AUC from 0.113 to 0.484; node embeddings (DeepWalk and node2vec with a hand-verified p and q transition, the matrix-factorization view, and a measured transductive limit where embeddings from two snapshots do not line up and add nothing); message passing (GCN with a hand-checked symmetric normalization, GraphSAGE mean aggregation and inductive use, GAT attention, over-smoothing measured as a 480-fold fall in Dirichlet energy over 8 propagation steps and as PR-AUC versus depth, heterogeneous R-GCN and metapath graphs, temporal graphs in brief); scaling (neighbor sampling arithmetic of 1 + 15 + 150 = 166 nodes per target, the measured cost of sampling at inference, sampling variance verified against the finite-population formula, Cluster-GCN, PinSage lessons from memory, precomputation and graph feature stores); link prediction and recommendation (LightGCN propagation by hand, negatives, evaluation, and a measured random-edge-split leak where 29.1 percent of test pairs already appear in training versus 9.1 percent under a time split); fraud and anomaly detection on graphs (neighbor-label leakage measured as PR-AUC 0.508 as-of versus 0.912 leaked, rings, temporal splits); knowledge graphs (triples, TransE scored by hand, ComplEx, R-GCN, GraphRAG in one paragraph, all labeled not measured); evaluation and leakage; when not to use a GNN (serving latency arithmetic with explicit assumptions); and a worked example comparing tabular, graph-feature, neighbor-aggregate and GraphSAGE models under a time-aware split with honest reporting of which won.
Practice questions (6)
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Graph Features or a GNN for Telecom Churn?
Intermediate · Free -
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Design an Inductive, Time-Aware Split for a Marketplace Graph
Advanced · Free -
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Build a Fraud-Ring Graph Pipeline Without Blowing Up on Hubs
Advanced -
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Explain PinSage-Style Neighbor Sampling to a Skeptical Interviewer
Advanced -
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Use a Knowledge Graph to Fill Missing Product Attributes
Intermediate