GCN โ€” Cora citation network

A 2-layer Graph Convolutional Network (Kipf & Welling 2017) trained on the Cora citation graph for 7-class node classification. Released as xaitalk's cross-framework XAI demo on graph architectures.

Files

File Format Size
gcn_cora.pt PyTorch state_dict ~110 KB

Architecture

Property Value
Layers 2 (graph convolution + dropout + graph convolution)
Hidden dim 16
Input features 1433 (word-vector features)
Output 7-class logits
Parameters ~23 K

Standard GCN (Kipf 2017). Test accuracy: 81.7% on the canonical Cora split (140 train / 500 val / 1000 test nodes).

Cross-framework verification

These weights are validated by xaitalk's gnn benchmark on Cora (19 methods):

Methods Passing at r โ‰ฅ 0.95 Min(min_r) Verified
19 19/19 0.9756 2026-05-09

The min_r=0.9756 sits on the stochastic vargrad method (variance-of-gradients) at default n_samples=50. All deterministic methods (full gradient family, full LRP family, DeepLIFT, GradCAM) at r โ‰ฅ 0.999 cross-framework.

Usage

from xaitalk.hub import ensure_model
import torch

ckpt_path = ensure_model('gnn/gcn-cora')

# Architecture class lives in xaitalk
from xaitalk.models import GCN
model = GCN(in_features=1433, hidden=16, num_classes=7)
model.load_state_dict(torch.load(ckpt_path, weights_only=True))
model.eval()

# Run XAI on a Cora subgraph
import xaitalk
expl = xaitalk.explain(model, x, method='lrp_epsilon', target_class=2)

Training data

Cora citation network โ€” 2708 papers, 7 classes (machine-learning subfields), 5429 citation edges, word-vector features per paper.

License

Apache 2.0.

Citation

@inproceedings{kipf2017gcn,
  author    = {Kipf, Thomas N. and Welling, Max},
  title     = {Semi-Supervised Classification with Graph Convolutional Networks},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2017}
}
@software{paul2026xaitalk,
  author = {Paul, Alexander},
  title  = {xaitalk: Cross-Framework Explainable AI Library},
  year   = {2026},
  url    = {https://xaitalk.com}
}

Links

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