SIGMA is a unified streaming graph partitioner supporting configurable vertex- and edge-balanced partitioning for distributed GNN training across different system architectures.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A robust design selector minimizes worst-case planning risk over an ambiguity set of exposure mechanisms, with Wasserstein bounds and selector theorems, yielding different recommendations on public datasets.
citing papers explorer
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SIGMA: A Versatile Streaming Graph Partitioner for Vertex- and Edge-Balanced Distributed GNN Training
SIGMA is a unified streaming graph partitioner supporting configurable vertex- and edge-balanced partitioning for distributed GNN training across different system architectures.
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Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems
A robust design selector minimizes worst-case planning risk over an ambiguity set of exposure mechanisms, with Wasserstein bounds and selector theorems, yielding different recommendations on public datasets.