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  "Title": "Spatial Projection of Network Signals along Geodesic Paths",
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  "Authors@R": "c(person(\"Sysbiolab Team\", role = \"aut\"),\nperson(\"Victor\", \"Apolonio\", role = \"ctb\"),\nperson(\"Jonathan\", \"Back\", role = \"ctb\"),\nperson(\"Lana\", \"Querne\", role = \"ctb\"),\nperson(\"Vinicius\", \"Chagas\", role = \"ctb\"),\nperson(\"Bahar\", \"Tercan\", role = \"ctb\"),\nperson(\"Mauro\", \"Castro\", role = c(\"cre\"),\nemail = \"mauro.a.castro@gmail.com\",\ncomment = c(ORCID = \"0000-0003-4942-8131\"))\n)",
  "Description": "For a given graph containing vertices, edges, and a signal\nassociated with the vertices, the 'PathwaySpace' package\nperforms a convolution operation, which involves a weighted\ncombination of neighboring vertices and their associated\nsignals. The package uses a decay function to project these\nsignals, creating geodesic paths on a 2D-image space.\n'PathwaySpace' has various applications, such as visualizing\nnetwork data in a graphical format that highlights the\nrelationships and signal strengths between vertices. By\ncombining graph theory, signal processing, and visualization,\n'PathwaySpace' provides a way of representing graph data on a\ncontinuous projection space. Based on methods introduced in\nTercan et al. (2025) <doi:10.1016/j.xpro.2025.103681> and\nEllrott et al. (2025) <doi:10.1016/j.ccell.2024.12.002>.",
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