Topics — The twenty-eight kinds of claim this anthology files against — the primary axis of both the practice registry and the reading list, and the same twenty-eight for each (ADR-026). The filing rule is the point: take a domain topic when the claim is ABOUT the domain; a claim merely discovered in one still takes its kind. The axis organizes the record, it does not bound it: content the list cannot place is evidence the list is short (ADR-059).
Graphs and networks — graph-structured data and the models that run on it — message passing and graph neural networks, how they are evaluated, graph curvature, rewiring and over-squashing, community structure and partitioning.
2 of 449 SOTA documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| SOTA-350 | Compare GNN architectures over many random data splits and seeds, with one shared training and hyperparameter-selection procedure for every model, never on a single fixed split | Shchur et al. (2018), LIT-581 — on the Planetoid split GAT beats GCN. On another random split of the same size GCN wins, and averaged over 100 splits × 20 seeds with identical training and tuning, GCN ranks first of four GNNs. A single split selects the model that overfits it, and per-model recipes measure the recipe. Report mean and spread over many splits and seeds, with the same tuning budget for every model. | Active |
| SOTA-351 | In a message-passing GNN for graph-level tasks, aggregate neighbours by sum with an MLP update, not by mean or max, when graph structure rather than node features carries the signal | Xu et al. (2019), LIT-580 — mean and max aggregation cannot count neighbors, so graphs that differ only in counts look the same to them. On featureless Reddit graphs, mean-aggregation GNNs are at chance (50.0, 20.0) and sum–MLP scores 92.4 and 57.5. With informative node features the choice matters much less, and results are mostly within noise. Use h = MLP((1 + ε)h + Σ neighbors) with ε = 0 and a sum readout. | Active |