This paper presents an AI-driven framework for validating graph-based datasets by detecting inconsistencies and anomalies. HCLTech’s algorithm uses node embedding and relationship inference to ensure data integrity in knowledge graphs and network models. The approach improves accuracy, scalability, and automation for applications in fraud detection, social network analysis, and recommendation systems.

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