🌌 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

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🎯 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

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🚀 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

🔍 Click to expand detailed results

📊 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

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🏗️ 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

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🔬 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

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

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📊 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

📊 Click to view detailed analysis

🧩 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

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🏆 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

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📚 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}
}

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🤝 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

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🛡️ License & Ethics

🔒 Responsible AI Development

• ✅ Privacy-preserving implementations
• ✅ Bias-aware model design
• ✅ Transparent algorithmic decisions
• ✅ Reproducible research standards

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🌟 Join the Graph Revolution

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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."

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⭐ Star us on GitHub | 📧 Contact: [email protected] | 🌐 Lab: HKU Data Science