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✨ 🔥 Heterogeneous Graph Intelligence | ⚡ Latent Diffusion | 🌊 Noise Denoising 🌊 ✨
🌟 Advancing Heterogeneous Graph Intelligence through Novel Latent Diffusion Strategies
"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."
🔬 The Heterogeneous Graph Diffusion Pipeline: From Noisy Reality to Pure Intelligence
| 🔥 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 |
| 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% 💪 |
🔍 Click to expand detailed 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% |
| 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 |
🌌 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
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))
- 🌟 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
| 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
| 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 |
📊 Click to view detailed analysis
| 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% |
| Behavior | DiffGraph Retention | HGT Retention |
|---|---|---|
| Page View | 97.42% | 95.59% |
| Favorite | 98.62% | 97.22% |
| Cart | 96.73% | 95.82% |
- Sparse Users (< 8 interactions): +31.4% improvement
- Medium Users (< 35 interactions): +25.1% improvement
- Active Users (< 120 interactions): +19.4% improvement
| 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
@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}
}🎯 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
🔒 Responsible AI Development
- ✅ Privacy-preserving implementations
- ✅ Bias-aware model design
- ✅ Transparent algorithmic decisions
- ✅ Reproducible research standards
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║ 🚀 Star this repository if DiffGraph powers your research! ║
║ 🔬 Open issues for scientific discussions and improvements ║
║ 🤝 Contribute to the future of heterogeneous graph AI ║
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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