Add paper link and Github link

README.md

@@ -1,3 +1,75 @@
 ---
 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)