City-Networks

Los Angeles

Node Classification PyG dataset page ↗PyG ↗

Structure

1Graphs
241,000Nodes
343,000Edges
37Node Feature Dim
25Edge Feature Dim
0.75Homophily

Coverage

10Results
1Variants
1Papers
Node classification on the Los Angeles 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

Los Angeles 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

Los Angeles 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
Exphormer
0.6380 ± 0.0060
2
ChebNet
0.6140 ± 0.0040
3
GraphSAGE
0.6140 ± 0.0030
4
SGFormer
0.6010 ± 0.0070
5
GraphGPS
0.5980 ± 0.0050
6
GAT
0.5950 ± 0.0030
7
GCN
0.5830 ± 0.0030
8
GCNII
0.5600 ± 0.0030
9
DropEdge
0.5550 ± 0.0030
10
MLP
0.2410 ± 0.0050
SOTA performance set a new SOTA at the time matched the SOTA at the time