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We evaluate IntCEG on two benchmark datasets:

Repacks are a great way to "demo" a game or save data, but purchasing the game officially supports the creators and ensures you get official online features.

We utilize a Graph Attention Network (GAT) to learn node representations. The attention mechanism allows the model to weigh the importance of neighboring nodes dynamically. For a target event node $v_i$, the embedding $h_i$ is updated by attending to its neighbors $N(v_i)$:

Inceg | Repack

We evaluate IntCEG on two benchmark datasets:

Repacks are a great way to "demo" a game or save data, but purchasing the game officially supports the creators and ensures you get official online features. inceg repack

We utilize a Graph Attention Network (GAT) to learn node representations. The attention mechanism allows the model to weigh the importance of neighboring nodes dynamically. For a target event node $v_i$, the embedding $h_i$ is updated by attending to its neighbors $N(v_i)$: We evaluate IntCEG on two benchmark datasets: Repacks