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---
license: cc-by-4.0
task_categories:
- graph-ml
tags:
- multimodal
- attributed-graph
- benchmark
---
# MAGB
This repository contains the Multimodal Attribute Graph Benchmark (MAGB) datasets described in the paper [When Graph meets Multimodal: Benchmarking on Multimodal Attributed Graphs Learning](https://huggingface.co/papers/2410.09132).
[Github repository](https://github.com/sktsherlock/MAGB)
MAGB provides 5 datasets from E-Commerce and Social Networks, and evaluates two major learning paradigms: _**GNN-as-Predictor**_ and **_VLM-as-Predictor_**. The datasets are publicly available on Hugging Face: [https://huggingface.co/datasets/Sherirto/MAGB](https://huggingface.co/datasets/Sherirto/MAGB).
Each dataset consists of several parts:
- Graph Data (*.pt): Stores the graph structure, including adjacency information and node labels. Loadable using DGL.
- Node Textual Metadata (*.csv): Contains node textual descriptions, neighborhood relationships, and category labels.
- Text, Image, and Multimodal Features (TextFeature/, ImageFeature/, MMFeature/): Pre-extracted embeddings from the MAGB paper for different modalities.
- Raw Images (*.tar.gz): A compressed folder containing images named by node IDs. Requires extraction before use. The Reddit-M dataset is particularly large and may require special handling (see Github README for details).
## 📖 Table of Contents
- [📖 Introduction](#-introduction)
- [💻 Installation](#-installation)
- [🚀 Usage](#-usage)
- [📊 Results](#-results)
- [🤝 Contributing](#-contributing)
- [❓ FAQ](#-faq)
---
## 📖 Introduction
Multimodal attributed graphs (MAGs) incorporate multiple data types (e.g., text, images, numerical features) into graph structures, enabling more powerful learning and inference capabilities.
This benchmark provides:
✅ **Standardized datasets** with multimodal attributes.
✅ **Feature extraction pipelines** for different modalities.
✅ **Evaluation metrics** to compare different models.
✅ **Baselines and benchmarks** to accelerate research.
---
## 💻 Installation
Ensure you have the required dependencies installed before running the benchmark.
```bash
# Clone the repository
git clone https://github.com/sktsherlock/MAGB.git
cd MAGB
# Install dependencies
pip install -r requirements.txt
```
## 🚀 Usage
### 1. Download the datasets from [MAGB](https://huggingface.co/datasets/Sherirto/MAGB). 👐
```bash
cd Data/
sudo apt-get update && sudo apt-get install git-lfs && git clone https://huggingface.co/datasets/Sherirto/MAGB .
ls
```
Now, you can see the **Movies**, **Toys**, **Grocery**, **Reddit-S** and **Reddit-M** under the **''Data''** folder.
<p align="center">
<img src="Figure/Dataset.jpg" width="900"/>
<p>
### 2. GNN-as-Predictor
...(rest of the content from Github README can be pasted here) |