Network science (Cambridge University Press)
PulseAugur coverage of Network science (Cambridge University Press) — every cluster mentioning Network science (Cambridge University Press) across labs, papers, and developer communities, ranked by signal.
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Deep graph generative models show promise for realistic network simulation
A new paper explores the effectiveness of deep graph generative models in creating realistic synthetic networks for research. By analyzing these models from a network science perspective, the study found that certain de…
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Review paper details optimal transport for network comparison in ML
A new review paper explores the application of optimal transport methods for comparing networks, particularly in machine learning contexts. The paper details three primary distances: Wasserstein, Gromov-Wasserstein, and…
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New Research Benchmarks Hyperbolic Graph Embedders for Network Analysis
A new paper published on arXiv benchmarks thirteen unsupervised hyperbolic graph embedders from machine learning, network science, and algorithmics. The study evaluates these methods for link prediction and topology rec…
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Neural network structure and depth impact learning performance
A new research paper explores how the structure of neural networks, specifically their modularity and depth, impacts learning performance. The study found that networks with densely interconnected communities, similar t…