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Found 3 papers, 3 papers with code
No Need to Look Back: An Efficient and Scalable Approach for Temporal Network Representation Learning
This strategy is implemented using a GPU-executable size-constrained hash table for each node, recording down-sampled recent interactions, which enables rapid response to queries with minimal inference latency.
Learning Fair Representations with High-Confidence Guarantees
Representation learning is increasingly employed to generate representations that are predictive across multiple downstream tasks.
Neighborhood-aware Scalable Temporal Network Representation Learning
Such a dictionary representation records a downsampled set of the neighboring nodes as keys, and allows fast construction of structural features for a joint neighborhood of multiple nodes.
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