**Technical Challenge: Transformer-based Temporal Reasoning

Technical Challenge: Transformer-based Temporal Reasoning with Memory-Augmented Graph Attention

In this challenge, we will tackle a novel problem in temporal reasoning using graph attention mechanisms, specifically designed for memory-augmented graph neural networks. Your task is to implement a Transformer-based model that incorporates a graph attention mechanism and a external memory component to perform temporal reasoning on a dynamic graph-structured dataset.

Dataset:

The dataset consists of a sequence of graph-structured observations, where each node in the graph has a binary attribute indicating whether it is active or inactive. The target is to predict the next state of the graph, given the past observations. The graph structure is dynamic, meaning that new nodes may be added or removed at each time step.

Constraints:

1. Temporal Context Window: The model must be able to capture temporal dependencies within a window of 10 time steps.
2. Graph Size: The maximum number of nodes in the graph is limited to 100.
3. Memory-Augmentation: The model must incorporate an external memory component that can store and retrieve information about the graph structure and node attributes.
4. Transformer-based Architecture: The model must use a Transformer-based architecture, specifically a Transformer encoder for processing the input sequence and a Transformer decoder for predicting the next graph state.
5. Graph Attention Mechanism: The model must use a graph attention mechanism to compute attention weights for nodes in the graph, taking into account the node attributes and graph structure.

Evaluation Metrics:

1. Accuracy: The model's ability to predict the correct next graph state.
2. F1-score: The model's ability to identify active nodes in the next graph state.
3. Memory Utilization: The model's ability to efficiently use the external memory component.

Submission Guidelines:

1. Implement the Transformer-based model using a deep learning framework of your choice (e.g., PyTorch, TensorFlow).
2. Provide a detailed description of the model architecture, including the graph attention mechanism and external memory component.
3. Evaluate the model on the provided dataset and report the results using the specified evaluation metrics.
4. Share your code and results in a public repository or on a platform of your choice.

Prizes:

1. Best Accuracy: A $1000 prize for the model with the highest accuracy on the evaluation task.
2. Best F1-score: A $750 prize for the model with the highest F1-score on the evaluation task.
3. Best Memory Utilization: A $500 prize for the model that achieves the best memory utilization while maintaining competitive performance on the evaluation task.

Deadline: February 28, 2026

Get ready to showcase your expertise in Transformer-based temporal reasoning with memory-augmented graph attention!

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