# Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning

arXiv: https://arxiv.org/abs/2412.17271 · HTML: https://mlsanity.com/paper/2412.17271/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Everything is Connected: Graph Neural Networks](https://mlsanity.com/paper/2301.08210/) (2023)
- [Towards Semi-supervised Universal Graph Classification](https://mlsanity.com/paper/2305.19598/) (2023)
- [Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction](https://mlsanity.com/paper/2410.20253/) (2024)
- [Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspective](https://mlsanity.com/paper/2411.14654/) (2024)
- [Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling](https://mlsanity.com/paper/2411.07482/) (2024)
- [Contextual Combinatorial Beam Management via Online Probing for Multiple Access mmWave Wireless Networks](https://mlsanity.com/paper/2412.10385/) (2024)
- [Improvement and Implementation of a Speech Emotion Recognition Model Based on Dual-Layer LSTM](https://mlsanity.com/paper/2411.09189/) (2024)
- [FGATT: A Robust Framework for Wireless Data Imputation Using Fuzzy Graph Attention Networks and Transformer Encoders](https://mlsanity.com/paper/2412.01979/) (2024)

## Built on by (most cited first, 2 of 2)

- [RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations](https://mlsanity.com/paper/2509.06286/) (2025)
- [TAS-GNN: A Status-Aware Signed Graph Neural Network for Anomaly Detection in Bitcoin Trust Systems](https://mlsanity.com/paper/2603.13290/) (2026)

## Abstract (arXiv)

Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, existing models struggle to effectively capture dependencies from multiple perspectives, limiting their ability to model complex data. To address this gap, we propose the Multi-view Fuzzy Graph Attention Network (MFGAT), a novel framework that constructs and aggregates multi-view information using a specially designed Transformation Block. This block dynamically transforms data from multiple aspects and aggregates the resulting representations via a weighted sum mechanism, enabling comprehensive multi-view modeling. The aggregated information is fed into FGAT to enhance fuzzy graph convolutions. Additionally, we introduce a simple yet effective learnable global pooling mechanism for improved graph-level understanding. Extensive experiments on graph classification tasks demonstrate that MFGAT outperforms state-of-the-art baselines, underscoring its effectiveness and versatility.