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.
6 of 737 LIT documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| LIT-386 | SuperGlue: Learning Feature Matching with Graph Neural Networks | Sarlin et al. (2019), ARXIV-1911.11763. Match two sets of local features by learning the matcher rather than hand-designing it: a graph neural network predicts assignment costs and a differentiable optimal transport step solves them, finding correspondences and rejecting unmatchable points in one operation. Replaces “hand-designed heuristics” with priors over geometry learned end to end — and still consumes keypoints somebody else detected. | Active |
| LIT-579 | Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark | Tönshoff, Ritzert, Rosenbluth, Grohe (2023), ARXIV-2309.00367. On the Long-Range Graph Benchmark, graph transformers were reported to beat message-passing GNNs. After a basic hyperparameter sweep within the same 500k-parameter budget, GCN, GINE and GatedGCN all beat GPS on Peptides-func and Peptides-struct. Most of the gain comes from a 2-layer prediction head instead of a linear one. On the superpixel datasets, input feature normalization accounts for at least half the gain of most models, and GPS stays ahead there. The link-prediction metric’s code computed raw MRR where the paper specified filtered MRR. | Active |
| LIT-580 | How Powerful are Graph Neural Networks? | Xu, Hu, Leskovec, Jegelka (ICLR 2019), ARXIV-1810.00826. Message-passing GNNs are at most as discriminative as the 1-WL isomorphism test, and they reach that bound when aggregation and readout are injective on multisets. Sum aggregation with an MLP update (GIN) is; mean (GCN) and max (GraphSAGE) are not. On featureless Reddit graphs, mean-aggregation GNNs are at chance (50.0, 20.0) and GIN scores 92.4 and 57.5. On the other seven benchmarks, most differences are within a standard deviation. | Active |
| LIT-581 | Pitfalls of Graph Neural Network Evaluation | Shchur, Mumme, Bojchevski, Günnemann (R2L workshop, NeurIPS 2018), ARXIV-1811.05868. GCN, GAT, MoNet and GraphSAGE were run with one shared training and tuning procedure, over 100 random splits × 20 seeds on 8 datasets. GCN has the best average rank (2.3) and relative accuracy (99.4%). On Planetoid’s single split GAT wins Cora and Citeseer; on another random split of the same sizes GCN wins them. GAT’s lower average on Amazon comes from 138 of 2,000 runs collapsing. | Active |
| LIT-582 | Semi-Supervised Classification with Graph Convolutional Networks | Kipf and Welling (ICLR 2017), ARXIV-1609.02907. The GCN layer is H′ = σ(D̃^{-1/2}ÃD̃^{-1/2} H W), with self-loops added and the adjacency symmetrically normalized. It is motivated as a first-order approximation of spectral graph convolution. Two layers reach 81.5% on Cora, 70.3% on Citeseer and 79.0% on Pubmed, all on the single Planetoid split. Models deeper than about 7 layers are hard to train without residual connections. | Active |
| LIT-766 | Community Detection on Networks with Ricci Flow | Ni et al. (2019), ARXIV-1907.03993 (Scientific Reports). Ollivier–Ricci curvature on a graph comes from optimal transport between the neighbourhoods of an edge’s endpoints. Edges between communities come out negatively curved and edges inside them positively curved. A discrete Ricci flow stretches negative edges and shrinks positive ones, and thresholding the resulting weights (“surgery”) separates the communities. On a 500-node, two-block SBM, ARI goes from nearly 100% at p_inter/p_intra = 0.5 to nearly 0% at 0.55. On LFR graphs it is the most stable of the methods compared. | Active |