City-Networks

London

Node Classification PyG dataset page ↗PyG ↗

Node classification on the London road graph, predicting an accessibility class for every intersection. Street segments are edges and labels depend on long-range network structure. Evaluated by Accuracy.

Dataset details

Task

London road-network node classification from the City-Networks benchmark. Labels are derived from long-range accessibility statistics on the city graph. The primary catalog metric is Accuracy.

Data

London road-network node classification from the City-Networks benchmark. Labels are derived from long-range accessibility statistics on the city graph. The cataloged artifact reports 1 graphs.

Features

Road-network intersections are nodes and street segments are edges. The release derives node inputs and accessibility-based labels from the embedded city network; adding coordinates, centralities, or external map attributes changes the feature set.

Splits and evaluation

The catalog records these protocols or variants: Standard split. Evaluation uses Accuracy (higher is better). Exact masks or folds must come from the cited release.

Quirks and pitfalls

The four cities differ in topology, sampling, and label distribution, so they are separate datasets rather than interchangeable folds. Labels are derived from the same graph, creating a risk of target leakage through engineered centrality or accessibility features.

Sources

SOTA Progression

new SOTA matches SOTA at that time other proposed result other baseline result

Leaderboard

#ModelAccuracy ↑Paper
1
GatedGCN
0.6147 ± 0.0014
2
GINE
0.5760 ± 0.0020
3
GAT
0.5719 ± 0.0109
4
GPS + k-MIP
0.5605
5
GraphSAGE
0.5540 ± 0.0020
6
GCN
0.5525 ± 0.0006
7
ChebNet
0.5470 ± 0.0020
8
GPS + Performer
0.5320
9
GAT
0.5200 ± 0.0030
10
GCN
0.5010 ± 0.0070
11
Exphormer
0.4950 ± 0.0040
12
SGFormer
0.4830 ± 0.0030
13
GCNII
0.4820 ± 0.0030
14
DropEdge
0.4500 ± 0.0030
15
MLP
0.2790 ± 0.0010
SOTA performance set a new SOTA at the time matched the SOTA at the time