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Neighborhood-Order Learning Graph Attention Network for Fake News Detection

Neighborhood-Order Learning Graph Attention Network for Fake News Detection

Authors: Batool Lakzaei, Mostafa Haghir Chehrghani, Alireza Bagheri Affiliation: Amirkabir University of Technology (Tehran Polytechnic), Iran Preprint: arXiv 2502.06927, February 2025

TL;DR

Proposes NOL-GAT (Neighborhood-Order Learning Graph Attention Network), a GNN architecture that allows each node to adaptively select its optimal hop-neighborhood at each layer, bypassing the fixed L-hop limitation of conventional message-passing GNNs. Evaluated on five fake news datasets, NOL-GAT outperforms baseline GNN methods, especially in low-data regimes (10%–30% labeled).

Contributions

  • Introduces an adaptive GNN architecture where each node independently determines its neighborhood order at every layer via a differentiable Gumbel-Softmax mechanism.
  • Proposes a two-component design: a Hop Network (Φ) that learns optimal neighborhood orders, and an Embedding Network (Ψ) that updates node representations using those neighborhoods.
  • Addresses core GNN limitations: over-smoothing, over-squashing, and excessive computational cost by selective neighbor aggregation.
  • Demonstrates consistent improvements over semi-supervised baselines (CO-GNN, L2Q, TGNcl, TextGCN, LSTM-CP-GCN, GATv2) across five datasets with varying label scarcity.

Method

The NOL-GAT pipeline consists of four stages:

  1. Feature extraction: News text is embedded using Doc2Vec, generating 500-dimensional vectors.
  2. Graph construction: A K-nearest-neighbor similarity graph is built, connecting each news item to its K nearest neighbors by cosine similarity.
  3. NOL-GAT layers: For each node at each layer:
  4. The Hop Network predicts a probability distribution over possible neighborhood orders (1-hop, 2-hop, ..., d_g-hop, where d_g is graph diameter) using a GAT v2 backbone and Gumbel-Softmax sampling.
  5. The selected neighborhood (determined by the sampled hop count) is used to aggregate information via the Embedding Network (also GAT v2).
  6. This allows different nodes to use different neighborhood sizes without fixing the hop-distance globally.
  7. Classification: Final embeddings from layer L are fed to a Multi-Layer Perceptron for binary fake/real prediction.

The training uses binary cross-entropy loss on labeled examples; the Gumbel-Softmax estimator provides a differentiable approximation of discrete sampling.

Results

Evaluated on five fake news datasets with three label proportion scenarios (10%, 20%, 30% labeled):

Fake.Br (Portuguese, 7,200 articles): - 10% labeled: 0.8126 accuracy, 0.8139 macro-F1 - 30% labeled: 0.8287 accuracy, 0.8327 macro-F1 - Outperforms TextGCN (0.6235 / 0.6112) and Co-GNN (0.6391 / 0.6139)

Fact-checked News (Portuguese, 2,168 articles): - 10% labeled: 0.9358 accuracy, 0.9382 macro-F1 - 30% labeled: 0.9501 accuracy, 0.9513 macro-F1 - Next-best (Co-GNN): 0.9096 / 0.9282

FakeNewsNet (English, 7,003 articles): - 10% labeled: 0.8525 accuracy, 0.7772 macro-F1 - 30% labeled: 0.8589 accuracy, 0.7916 macro-F1 - Exceeds Co-GNN (0.8139 / 0.6662) and GATv2 (0.7986 / 0.6883)

FakeNewsDetection (English, 3,988 articles): - 10% labeled: 0.8892 accuracy, 0.8908 macro-F1 - 30% labeled: 0.9014 accuracy, 0.9044 macro-F1 - Competitive edge over Co-GNN (0.8210 / 0.7953)

FakeNewsData (English, 20,700 articles): - 10% labeled: 0.8837 accuracy, 0.8837 macro-F1 - 30% labeled: 0.8947 accuracy, 0.8957 macro-F1 - Outperforms Co-GNN (0.8703 / 0.8703) and GATv2 (0.8513 / 0.8513)

Connections

  • Related to Propagation-based fake news detection through graph-structured modeling of news similarity.
  • Extends Graph Neural Networks by enabling per-node adaptive aggregation depth, addressing over-squashing and over-smoothing identified in deep message-passing literature.
  • Shares semi-supervised learning methodology with Semi Supervised Learning.
  • Applies graph attention networks as backbone for both hop-order prediction and embedding updates.
  • Addresses limitations of message-passing neural networks by enabling adaptive per-node neighborhood selection.

Notes

Strengths: - Novel approach to a concrete GNN limitation (fixed L-hop neighborhoods). The Gumbel-Softmax mechanism for differentiable discrete choice is well-motivated. - Strong empirical results across five datasets with multiple label regimes. Particularly impressive in low-data settings (10% labeled). - Clear problem motivation: explains why standard GNNs fail to leverage distant but important nodes, and why simply adding more layers doesn't work.

Limitations: - Still a preprint (February 2025); peer-review status unknown. - Computational complexity analysis sparse. The overhead of running the Hop Network at every node in every layer is not compared quantitatively to baselines. - Graph construction (K-NN with cosine similarity) is simplistic; other similarity measures or learned graph structures are not explored. - Content-only approach (text embeddings only); ignores user profiles, sharing patterns, or temporal dynamics that may carry signal in real-world deployment. - Evaluation limited to text-based fake news detection. Transferability to multimodal (image + text) disinformation unknown.