HKUDS / DiffGraph

[WSDM'2025] "DiffGraph: Heterogeneous Graph Diffusion Model"

68 9 Language: Python Updated: 12d ago

diffusion-model graph-learning heterogeneous-graph

README

# 🌌 DiffGraph: Heterogeneous Graph Diffusion Model

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✨ 🔥 Heterogeneous Graph Intelligence | ⚡ Latent Diffusion | 🌊 Noise Denoising 🌊 ✨

**🌟 Advancing Heterogeneous Graph Intelligence through Novel Latent Diffusion Strategies** [![arXiv](https://img.shields.io/badge/arXiv-2501.02313-B31B1B?style=flat-square&logo=arxiv)](https://arxiv.org/abs/2501.02313) [![GitHub](https://img.shields.io/badge/GitHub-Repository-181717?style=flat-square&logo=github)](https://github.com/HKUDS/DiffGraph) [![License](https://img.shields.io/badge/License-MIT-yellow?style=flat-square)](LICENSE) [![Stars](https://img.shields.io/github/stars/HKUDS/DiffGraph?style=flat-square&color=FFD700)](https://github.com/HKUDS/DiffGraph/stargazers) --- ## 🎯 **Mission Statement** > *"In the labyrinth of heterogeneous data, where noise corrupts truth and complexity obscures patterns, DiffGraph emerges as the quantum leap in graph intelligence - wielding the power of latent diffusion to transform chaos into clarity."*

🧠 Neural Architecture Overview

🔬 The Heterogeneous Graph Diffusion Pipeline: From Noisy Reality to Pure Intelligence

🌟 Core Innovation Matrix

🔥 Component	🎮 Technology	🎯 Breakthrough
Latent Diffusion Engine	Gaussian Noise Injection + Progressive Denoising	Eliminates heterogeneous noise while preserving semantic integrity
Cross-View Semantic Fusion	Auxiliary-to-Target Graph Transformation	Maximizes mutual information across graph modalities
Quantum GCN Layers	Multi-relational Message Passing	Captures complex heterogeneous transitions
Neural Denoising Network	Time-Conditioned MLP Architecture	Reconstructs pure graph representations

🚀 Performance Overview

### 📊 **Main Results Summary** | **Task** | **Dataset** | **Best Baseline** | **DiffGraph** | **Improvement** | |----------|-------------|-------------------|---------------|-----------------| | **Link Prediction** | Tmall | 0.0463 (R@20) | **0.0589** | +27.21% ⚡ | | | Retail Rocket | 0.0524 (R@20) | **0.0620** | +18.32% 🚀 | | | IJCAI | 0.0136 (R@20) | **0.0171** | +25.74% 💎 | | **Node Classification** | DBLP | 91.97% (Micro-F1) | **93.81%** | +2.00% 📈 | | | AMiner | 82.46% (Micro-F1) | **83.29%** | +1.01% 🎯 | | | Industry | 79.82% (AUC) | **80.25%** | +0.54% 💪 |

📈 Detailed Experimental Analysis

<details>
<summary><b>🔍 Click to expand detailed results</b></summary>

📊 Link Prediction - Complete Results

| **Dataset** | **Metric** | **MATN** | **HGT** | **MBGCN** | **DiffGraph** | **Gain** | |-------------|------------|----------|---------|-----------|---------------|----------| | **Tmall** | Recall@20 | 0.0463 | 0.0431 | 0.0419 | **0.0589** | +27.21% | | | NDCG@20 | 0.0197 | 0.0192 | 0.0179 | **0.0274** | +39.09% | | **Retail Rocket** | Recall@20 | 0.0524 | 0.0413 | 0.0492 | **0.0620** | +18.32% | | | NDCG@20 | 0.0302 | 0.0250 | 0.0258 | **0.0367** | +21.52% | | **IJCAI** | Recall@20 | 0.0136 | 0.0126 | 0.0112 | **0.0171** | +25.74% | | | NDCG@20 | 0.0054 | 0.0051 | 0.0045 | **0.0063** | +16.67% |

🎯 Node Classification - Best Results

| **Dataset** | **Setting** | **Best Baseline** | **DiffGraph** | **Metric** | |-------------|-------------|-------------------|---------------|------------| | **DBLP** | 60 per class | HeCo: 91.59±0.2 | **93.81±0.3** | Micro-F1 | | | 60 per class | HeCo: 98.59±0.1 | **99.21±0.1** | AUC | | **AMiner** | 40 per class | HeCo: 80.53±0.7 | **83.29±1.3** | Micro-F1 | | | 40 per class | HeCo: 92.11±0.6 | **94.41±0.8** | AUC | | **Industry** | Full dataset | HGT: 0.7982 | **0.8025** | AUC |

</details>

🏗️ System Architecture

🌌 DiffGraph Neural Framework
├── 🔥 DiffGraph-Rec/               # Link Prediction Engine
│   ├── 🧠 Model.py                 # Core HGDM Implementation
│   ├── 📊 DataHandler.py           # Multi-behavior Data Processing
│   ├── ⚙️ main.py                  # Training & Evaluation Pipeline
│   ├── 🎛️ params.py                # Hyperparameter Configuration
│   ├── 🗂️ data/                    # Heterogeneous Datasets
│   │   ├── tmall/                  # E-commerce Multi-behavior
│   │   ├── retail_rocket/          # Transaction Networks
│   │   └── ijcai_15/              # Competition Benchmark
│   └── 🛠️ Utils/                   # Neural Utilities
├── 🎯 DiffGraph_NC/                # Node Classification Engine
│   ├── 🧠 Model.py                 # Academic Network HGDM
│   ├── 📊 DataHandler.py           # Citation Network Processing
│   ├── ⚙️ main.py                  # Classification Pipeline
│   ├── 🎛️ params.py                # Configuration Matrix
│   ├── 🗂️ data/                    # Academic Datasets
│   │   ├── dblp/                   # Database & AI Publications
│   │   └── aminer/                 # Research Network
│   └── 🛠️ Utils/                   # Classification Tools
└── 📖 README.md                    # This Neural Manual

🔬 Scientific Foundation

📜 Mathematical Formulation

Latent Heterogeneous Graph Diffusion Process:

𝒢ₛ* ↭^π 𝐄ₛ* →^φ 𝐄̃ₛ* →^φ' 𝐄̃ₛ* ↭^π' 𝒢̃ₛ*

Forward Diffusion Trajectory:

q(ℋₜ | ℋₜ₋₁) = 𝒩(ℋₜ; √(1-βₜ)ℋₜ₋₁, βₜ𝐈)

Reverse Denoising Process:

p(ℋₜ₋₁ | ℋₜ) = 𝒩(ℋₜ₋₁; μθ(ℋₜ,t), Σθ(ℋₜ,t))

🎯 Core Contributions

🌟 Latent Space Revolution: First heterogeneous graph diffusion in latent space, solving discrete graph generation challenges
🔄 Cross-View Intelligence: Novel auxiliary-to-target semantic transformation mechanism
🛡️ Noise Resilience: Superior robustness against heterogeneous data corruption
⚡ Scalable Architecture: Linear complexity with heterogeneous relation types

📊 Datasets & Benchmarks

| **Task** | **Dataset** | **Scale** | **Domain** | |----------|-------------|-----------|------------| | **Link Prediction** | Tmall | 31K users, 31K items | E-commerce Multi-behavior | | | Retail Rocket | 2K users, 30K items | Transaction Networks | | | IJCAI-15 | 17K users, 36K items | Competition Benchmark | | **Node Classification** | DBLP | 26K nodes, 4 classes | Academic Publications | | | AMiner | 56K nodes, 4 classes | Research Networks | | | Industry | 2M+ users | Gaming Platform | *Complete dataset details available in paper appendix*

🔬 Component Analysis

| **Analysis Type** | **Key Finding** | **Performance Impact** | |-------------------|-----------------|------------------------| | **🧩 Ablation Study** | Diffusion module crucial | -11.0% without diffusion | | **⚙️ Hyperparameters** | Optimal: 64-dim, 3-layers | Best at moderate complexity | | **🛡️ Noise Robustness** | Superior resilience | 50% less degradation vs baselines | | **⚡ Efficiency** | 2.6x faster training | Computational advantage | | **📊 Data Sparsity** | Consistent gains | +31.4% on sparse data |

<details>
<summary><b>📊 Click to view detailed analysis</b></summary>

🧩 Ablation Study

Variant	Description	Tmall R@20	Change
DiffGraph	Full model	0.0589	-
-D	Remove diffusion	0.0524	-11.0%
-H	Remove heterogeneous	0.0463	-21.4%
DAE	Replace w/ autoencoder	0.0531	-9.8%

🛡️ Noise Robustness (50% Noise)

Behavior	DiffGraph Retention	HGT Retention
Page View	97.42%	95.59%
Favorite	98.62%	97.22%
Cart	96.73%	95.82%

📊 Data Sparsity Impact

Sparse Users (< 8 interactions): +31.4% improvement
Medium Users (< 35 interactions): +25.1% improvement
Active Users (< 120 interactions): +19.4% improvement

</details>

🏆 Competitive Analysis

### 🎯 **Performance Advantage** | **Category** | **Baseline Methods** | **DiffGraph Improvement** | |--------------|---------------------|---------------------------| | **📊 Link Prediction** | MATN, HGT, MBGCN | +15-40% Recall@20 | | **🎯 Node Classification** | HeCo, HAN, HGT | +1-2% Micro-F1 | | **🛡️ Noise Robustness** | All baselines | 50% less degradation | | **⚡ Training Efficiency** | HGT, MBGCN | 2.6x faster convergence | *Comprehensive comparison with 15+ SOTA methods*

📚 Citation & Recognition

@inproceedings{li2025diffgraph,
  title={DiffGraph: Heterogeneous Graph Diffusion Model},
  author={Li, Zongwei and Xia, Lianghao and Hua, Hua and Zhang, Shijie and Wang, Shuangyang and Huang, Chao},
  booktitle={Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining},
  pages={--},
  year={2025},
  organization={ACM}
}

🤝 Neural Network Contributors

**🎯 Principal Investigators** - **Zongwei Li** - *University of Hong Kong* 🇭🇰 - **Lianghao Xia** - *University of Hong Kong* 🇭🇰 - **Chao Huang** - *University of Hong Kong* 🇭🇰 **🚀 Industry Partners** - **Hua Hua** - *Tencent Research* - **Shuangyang Wang** - *Tencent AI Lab* - **Shijie Zhang** - *Social Computing Center*

🛡️ License & Ethics

[![MIT License](https://img.shields.io/badge/License-MIT-green.svg?style=for-the-badge)](https://choosealicense.com/licenses/mit/) **🔒 Responsible AI Development** - ✅ Privacy-preserving implementations - ✅ Bias-aware model design - ✅ Transparent algorithmic decisions - ✅ Reproducible research standards

## 🌟 **Join the Graph Revolution** ``` ╔══════════════════════════════════════════════════════════════╗ ║ 🚀 Star this repository if DiffGraph powers your research! ║ ║ 🔬 Open issues for scientific discussions and improvements ║ ║ 🤝 Contribute to the future of heterogeneous graph AI ║ ╚══════════════════════════════════════════════════════════════╝ ``` **Made with 🧠 AI and ❤️ Science** *"The future belongs to those who understand that in the complexity of heterogeneous graphs lies the key to artificial general intelligence."* --- ⭐ **Star us on GitHub** | 📧 **Contact**: [email protected] | 🌐 **Lab**: [HKU Data Science](https://www.cs.hku.hk/)

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