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We just added support for temporal-based link sampling within If you are batching multiple graphs, you can also leverage the seed_node_time = …
mask = edge_time < seed_node_time[batch[edge_index[0]]
edge_index = edge_index[:, mask] |
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Hi team,
I'm working on an edge classification problem with temporal constraints.
For example, given a supervision-edge from time t1 I would like to filter out all neighboring edges that came from t>t1 ("the future of t1") to prevent data leakage.
I'm having trouble performing this type of filtering on the batch level (meaning, filtering each supervision-edge's neighboring message-passing edges using the supervision-edges' timestamps) because the message-passing edges defined by
edge_index
are not stored separately for each sample in the batch. (edge_index
has a[2, num_edges]
dimensions).I tried the following two approaches:
edge_index
by the single supervision edge's timestamp.Worked extremely slow.
For each batch:
The loss is aggregated for each time window and the optimizer step happens at the end of each batch.
Works faster.
If anybody has a more elegant solution I would be happy to hear : )
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