Graph Algorithm Applications in Douyu Live Stream Anti‑Cheat: Architecture, Evolution, Modeling, and Case Studies

Introduction Douyu, a personal live‑streaming platform, faces significant challenges from fraudulent traffic generated by black‑market activities. This article introduces the use of graph algorithms in Douyu's anti‑cheat system, covering the risk scenarios, algorithm framework, evolution, modeling process, and practical implementations.

1. Douyu Traffic Risk Scenarios The platform’s primary risk stems from streamers inflating metrics such as popularity, followers, experience, and VIP status through fake traffic. Additional risks arise from marketing and acquisition campaigns, making traffic fraud a major concern for Douyu.

2. Douyu Graph Algorithm System The overall architecture consists of low‑level graph operators (neighbor sampling, random walk, sub‑graph extraction) optimized for performance. Built on these are standard graph algorithms: knowledge‑graph‑based and behavior‑synchrony graph construction, KNN‑based proximity graphs, label propagation, graph partitioning, and various embedding methods (Node2Vec, EGES, graph clipping). Applications focus on fraud detection, embedding generation for downstream tasks, and graph‑based recommendation recall.

3. Evolution of Douyu Graph Algorithms Started in 2018 with basic graph partitioning (connected components, data discovery, label propagation). Limitations included over‑reliance on graph construction and coarse community detection. In 2019, richer graph construction methods (knowledge‑graph reasoning, similarity‑based edges) and representation learning were introduced to capture implicit relationships. By 2020, a full‑scene gang‑mining algorithm unified multiple scenarios, improving coverage, reducing false positives, and enhancing interpretability.

4. Graph‑Based Anti‑Cheat Modeling Advantages of graph methods include: (a) capturing the clustered, time‑sensitive nature of fraudulent activity; (b) robustness against adversarial adaptation due to rich topological features; (c) natural handling of relational data. The modeling workflow comprises four stages:

① Graph Construction – Define nodes (accounts, devices, virtual entities) and generate edges via behavior synchrony, knowledge‑graph inference, or embedding‑based ANN similarity.

② Graph Tasks – Apply supervised or unsupervised learning; unsupervised methods (e.g., community detection) are preferred for gang discovery due to label scarcity and better interpretability.

③ Interpretability – Provide statistics of detected gangs (e.g., white‑user ratio, average level) and derive features from k‑hop neighborhoods to explain decisions.

④ Business Applications – Pre‑emptive gang interception, post‑incident handling, and risk scoring using graph embeddings as relational features.

5. Practical Cases

Case 1: Sequence‑Graph Gang Identification Events are concatenated into sequences per streamer, embedded via Word2Vec or EGES, aggregated with SIF weighting, and then linked via ANN to form a graph. Connected components reveal similar malicious sequences, enabling detection of popularity‑boosting and experience‑boosting gangs across multiple rooms.

Case 2: Full‑Scene Traffic Gang Mining Features are defined and distances computed per feature type. Single‑feature gangs are generated as nodes, then merged based on inter‑gang distances. Gang profiles (e.g., registration source proportion) guide metric‑learning‑based weighting, while outlier accounts are filtered to reduce false positives.

Conclusion By integrating all scenario data into a unified graph model, Douyu eliminates the need for separate scene‑specific models. Self‑learning and false‑positive mitigation mechanisms ensure accurate gang detection, while feature‑based gang profiles provide rapid interpretability, delivering significant improvements in anti‑cheat effectiveness.