GraSPy: Graph Statistics in Python

By Jaewon Chung, B. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein. 2019

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Tracking the perspectives of interacting language models (2024, methodology): “We use all-MiniLM-L6-v2 , a sentence embedding function from (Reimers and Gurevych, 2019) based on (Wang et al., 2020b) hosted on the HuggingFace Hub (Wolf et al., 2020), as the surrogate embedding function and the implementation of CMDS from Graspologic (Chung et al., 2019).”
Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval (2024, unknown): “We utilize Scikit-learn (Pedregosa et al., 2018) for agglomerative clustering and Graspy (Chung et al., 2019) for the hierarchical Leiden algorithm.”
Statistical inference on black-box generative models in the data kernel perspective space (2024, unknown): “We use the MDS implementation from Graspologic (Chung et al., 2019) throughout.”
Consistent estimation of generative model representations in the data kernel perspective space (2024, methodology): “5 [17] as the embedding model and GrasPy’s [6] implementation of multi-dimensional scaling throughout.”
Inducing a hierarchy for multi-class classification problems (2021, methodology): “The first is by clustering the conditional means via GMM as implemented by graspologic (Chung et al., 2019).”