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  "Title": "Community Detection by Scaled Null-Adjusted Persistence",
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  "Authors@R": "c(\nperson(\n\"Alessandro\", \"Avellone\",\nemail = \"alessandro.avellone@unimib.it\",\nrole = c(\"aut\", \"cre\")\n),\nperson(\n\"Paolo\", \"Bartesaghi\",\nemail = \"paolo.bartesaghi@unimi.it\",\nrole = c(\"aut\")\n),\nperson(\n\"Stefano\", \"Benati\",\nemail = \"stefano.benati@unitn.it\",\nrole = c(\"aut\")\n),\nperson(\n\"Rosanna\", \"Grassi\",\nemail = \"rosanna.grassi@unimib.it\",\nrole = c(\"aut\")\n)\n)",
  "Description": "Finds the vertex partition of an undirected graph that\nmaximises a persistence-based objective, using the Milano\nlocal-search algorithm. Three related quality measures are\nsupported: the persistence probability of a community, its\nnull-adjusted persistence (NAP), and the scaled null-adjusted\npersistence (Scaled-NAP), which interpolates between NAP and\nmodularity. The methods are described in Avellone et al. (2023)\n<doi:10.1007/s10288-023-00559-z> and Avellone et al. (2025)\n<doi:10.1016/j.ins.2025.123032>.",
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