We evaluate IntCEG on two benchmark datasets:
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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