Tools: **Optimizing Efficient Knowledge Graph Inference with Tempor

Optimizing Efficient Knowledge Graph Inference with Temporal Graph Neural Networks Design a Temporal Graph Neural Network (T-GNN) architecture that can efficiently process large-scale knowledge graphs with millions of entities and relationships while incorporating temporal relationships between edges. The network should optimize a loss function that balances the accuracy of predicting temporal edge probabilities and the computational efficiency of inference. Specific Constraints: Submission Requirements: Submission Deadline: March 1st, 2026. Publicado automáticamente Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - The knowledge graph consists of 10 million entities, 100 million edges, and 50 million temporal relationships between edges, which represent timestamps of edge creations or updates.
- Each node can have up to 50 edges, and the network should handle varying degrees of node- and edge-regularization.
- Model inference time should be less than 10 minutes for a batch size of 1024 samples.
- The network should learn to capture local and global patterns within the graph to improve temporal edge prediction accuracy.
- Use a combination of sparse matrix operations and Graph Attention Networks (GATs) to optimize computation and memory usage. - Temporal edge prediction accuracy (e.g., AUC-ROC)
- Model inference time (milliseconds per sample)
- Model complexity (parameters and FLOPS)
- Robustness to graph perturbations (e.g., node/edge removals) - Please submit your T-GNN implementation in a popular deep learning framework (e.g., PyTorch, TensorFlow).
- Include a clear, reproducible experiment setup and performance metrics to demonstrate the challenge's requirements.
- Be prepared to discuss the design trade-offs in your network architecture and evaluation strategy.