Graph Technology Overview and Applications – From GraphGPT to Graph Databases

GraphGPT aims to enable large language models to understand graph‑structured data and perform graph‑related tasks such as node classification and link prediction, addressing challenges of graph data diversity and model transfer by exploring graph input methods, model alignment, and inference enhancement.

The section on knowledge transfer for complex graphs discusses the data‑hungry problem, proposes using open‑domain data to supplement domain‑specific knowledge, examines distribution shift challenges, and introduces the KBL method for graph knowledge transfer in universal scenarios.

Graph technology in financial anti‑fraud is highlighted as increasingly critical; by constructing complex relational networks, it improves the precision and efficiency of detecting illicit activities, helps uncover clustered risks and hidden patterns, and safeguards financial security.

In telecom networks, graph learning is explored for performance optimization, outlining its advantages and challenges in areas such as financial risk control and commercial recommendation, and analyzing strategies to enhance graph learning efficiency while forecasting future trends.

The article also examines Graph Foundation Models (GFM) in the era of rapid LLM development, describing their concepts, characteristics, development progress, and team efforts, and emphasizing the broad potential of combining LLMs with graph models for cross‑domain applications.

Multi‑domain graph large models are applied in Baidu's recommendation system, where graph embeddings and graph neural network algorithms improve node classification and edge prediction tasks, thereby increasing recommendation accuracy; the paper shares background, common algorithms, and the evolution of Baidu's feed graph model.

Ant's TuGraph‑DB high‑availability architecture is introduced, covering the definition of high availability, architectural patterns, master‑slave replication, and achieving five‑nine reliability to ensure continuous service and data safety.

Kuaishou's graph database architecture is analyzed, focusing on storage‑compute separation and its use in real‑time recommendation recall; through graph diffusion and co‑follow scenarios, the design efficiently processes massive data, enhancing recommendation performance.

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