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  "Title": "Hierarchical Community Detection by Recursive Partitioning",
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  "Date": "2024-01-28",
  "Authors@R": "c(person(\"Tianxi Li\", email = \"tianxili@umn.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Lihua Lei\", email = \"lihualei@stanford.edu\", role = c(\"aut\")),\nperson(\"Sharmodeep Bhattacharyya\", email = \"sharmodeep.bhattacharyya@oregonstate.edu\", role = c(\"aut\")),\nperson(\"Purna Sarkar\", email = \"purna.sarkar@austin.utexas.edu\", role = c(\"aut\")),\nperson(\"Peter Bickel\", email = \"bickel.peter@gmail.com\", role = c(\"aut\")),\nperson(\"Elizeveta Levina\", email = \"elevina@umich.edu\", role = c(\"aut\")))",
  "Description": "Hierarchical community detection on networks by a\nrecursive spectral partitioning strategy, which is shown to be\neffective and efficient in Li, Lei, Bhattacharyya, Sarkar,\nBickel, and Levina (2018) <arXiv:1810.01509>. The package also\nincludes a data generating function for a binary tree\nstochastic block model, a special case of stochastic block\nmodel that admits hierarchy between communities.",
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  "Author": "Tianxi Li [aut, cre], Lihua Lei [aut], Sharmodeep Bhattacharyya\n[aut], Purna Sarkar [aut], Peter Bickel [aut], Elizeveta Levina\n[aut]",
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