Stochastic Block Models
PulseAugur coverage of Stochastic Block Models — every cluster mentioning Stochastic Block Models across labs, papers, and developer communities, ranked by signal.
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New topological framework characterizes GNNs for transfer learning
Researchers have developed a novel topological framework to analyze and compare trained Graph Neural Networks (GNNs). This method maps the induced Stochastic Block Models onto the unit n-sphere, creating a low-dimension…
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New algorithms improve community detection in hypergraphs
Researchers have developed new spectral algorithms for community detection in hypergraphs, improving upon existing methods for non-uniform models. One paper introduces a three-step spectral algorithm that achieves parti…
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New papers explore optimal transport for ML inference
Two new arXiv papers explore advanced inference techniques in machine learning. One paper benchmarks likelihood-free inference methods, evaluating their performance with heavy-tailed and discrete data. The other paper b…
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New algorithms tackle node-private community estimation in graphs
Researchers have developed new algorithms for community recovery in stochastic block models that incorporate node differential privacy. These methods are designed to be stable against node-wise changes in graph structur…