Graph Neural Networks: Theory and Applications in Risk Control

01 Data Introduction The ebook "Introduction to Graph Neural Networks and Risk Control Scenarios" produced by DataFun includes both theoretical knowledge of GNNs and practical applications in finance, e‑commerce, and internet risk control.

02 How to Get Scan the QR code below and reply with "GNN" to receive the free ebook before the deadline (2022‑12‑18).

03 Table of Contents

1. Algorithmic Techniques for Large‑Scale GNN Computation

Idea 1: Avoid \|E\|

Idea 2: Reduce D

Idea 3: Partial iterative updates (selectively reduce T)

Idea 4: Baking (temporary memory storage)

Idea 5: Distillation

Idea 6: Partition or clustering

Idea 7: Sparse graph computation

Idea 8: Sparse routing

Idea 9: Cross‑sample shared graph features

Idea 10: Combine the above methods

2. Adaptive Generalized PageRank GNN GNNs outperform traditional methods on various graph tasks and are being explored in biomedical domains, such as gene‑disease classification and drug repurposing.

3. Exploring the Expressive Power of GNNs Three criteria are discussed: distinguishing non‑isomorphic graphs, counting subgraphs, and counting feature‑aware walks. Based on these, limitations of existing models are shown and more expressive models like Ring‑GNN and Local Relational Pooling are introduced.

4. Fraud Detection with GNNs – From Research to Practice The article reviews academic papers on fraud detection, summarizes methodological insights, and provides resources and open‑source projects.

5. GNNs in Anti‑Fraud Applications Graph models monitor malicious web activity and help design strategies to combat black‑market operations.

6. GNN‑Based Internet Financial Fraud Detection Post‑COVID‑19 digitalization has increased online financial fraud; a discussion by Dr. Ao Xiang covers challenges and why GNNs are suitable, including pitfalls and future trends.

Post‑pandemic internet financial fraud

Why GNNs work

Pitfalls of using GNNs

Trends of GNNs

7. Real‑Time Risk Control with GNNs Describes how GNN operators construct temporal concepts for payment risk control, leveraging recent advances such as graph pre‑training and large‑scale graph partitioning.

8. GNNs in Payment Risk Control Explains how eBay’s global payment system uses GNNs to protect user funds, detect card theft, and defend against organized fraud.