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AI Is Mapping Hidden Connections—And It’s Just Getting Started
- In this post, the process of setting up an inference endpoint for link predictions using a trained GNN model for the Twitch social networking data in Neptune ML is described.
- To create the endpoint, the Neptune cluster's API and model artifacts in S3 are used, with specific IAM role and trust policy requirements.
- Curl commands are employed to establish the inference endpoint and select EC2 instance type for link prediction.
- The endpoint ID is obtained, and its status can be checked to ensure it is 'InService' before using it for queries.
- Queries are utilized to identify vertices with the highest number of connections and to predict potential new connections in the graph.
- Gremlin queries involving Neptune ML predicates are executed to get predictions with confidence thresholds and exclude existing connections.
- The predicted connections can be used for personalized recommendations, user engagement strategies, and targeted advertising in social networking datasets.
- Link prediction extends beyond social networking to various applications such as recommendation engines and network optimization, offering valuable insights and enhanced user experiences.
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