tag:joss.theoj.org,2005:/papers/tagged/network?page=3Journal of Open Source Software2023-08-11T13:24:55ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/44542023-08-11T13:24:55Z2023-08-12T00:01:15ZTDLM: An R package for a systematic comparison of trip distribution laws and modelsacceptedv0.1.02023-04-27 14:56:28 UTC882023-08-11 13:24:55 UTC820235434MaximeLenormandTETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France0000-0001-6362-347310.21105/joss.05434https://doi.org/10.5281/zenodo.8183755https://joss.theoj.org/papers/10.21105/joss.05434.pdfR, Spatial Interaction Models, Spatial networks, Commuting networks, Gravity model, Radiation modeltag:joss.theoj.org,2005:Paper/41632023-08-04T17:14:02Z2023-08-05T00:01:33ZCNATool - Complex Network Analysis Toolaccepted2.0.42023-01-14 22:39:11 UTC882023-08-04 17:14:02 UTC820235373RobertoLuiz SouzaMonteiroSENAI CIMATEC University Center, Brasil \newline, Universidade do Estado da Bahia, Brasil \newline0000-0002-3931-5953RenataSouza Freitas DantasBarretoUniversidade do Estado da Bahia, Brasil \newline0000-0002-1607-4800AndréiaRitada SilvaUniversidade do Estado da Bahia, Brasil \newline0009-0009-0587-1263Alexandredo NascimentoSilvaUniversidade do Estado da Bahia, Brasil \newline, Universidade Estadual de Santa Cruz, Brasil \newline0000-0001-7436-8818JoséRoberto Araújode FontouraUniversidade do Estado da Bahia, Brasil \newline0000-0002-9703-835XMarcosBatistaFigueredoUniversidade do Estado da Bahia, Brasil \newline0000-0002-8193-5419HernaneBorges Barrosde PereiraSENAI CIMATEC University Center, Brasil \newline, Universidade do Estado da Bahia, Brasil \newline0000-0001-7476-926710.21105/joss.05373https://doi.org/10.5281/zenodo.8206265JavaScripthttps://joss.theoj.org/papers/10.21105/joss.05373.pdfCNATool, complex network analysis, graph, network, degree, clustering, centrality, Pajektag:joss.theoj.org,2005:Paper/38282023-07-26T13:30:49Z2023-07-27T00:01:46ZAmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantificationacceptedv0.12022-08-22 16:32:43 UTC872023-07-26 13:30:49 UTC820235035MuhammedShuaibiDepartment of Chemical Engineering, Carnegie Mellon University, United StatesYugeHuDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United States0000-0003-3648-7749XiangyunLeiDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesBenjaminM.ComerDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesMattAdamsDepartment of Chemical Engineering, Carnegie Mellon University, United StatesJacobParasSchool of Physics and School of Computer Science, Georgia Institute of Technology, United StatesRuiQiChenDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesEricMusaDepartment of Chemical Engineering, University of Michigan, United StatesJosephMusielewiczDepartment of Chemical Engineering, Carnegie Mellon University, United StatesAndrewA.PetersonSchool of Engineering, Brown University, United States0000-0003-2855-9482AndrewJ.MedfordDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United States0000-0001-8311-9581ZacharyUlissiDepartment of Chemical Engineering, Carnegie Mellon University, United States0000-0002-9401-491810.21105/joss.05035https://doi.org/10.5281/zenodo.8151492Python, C++, C, G-code, GAPhttps://joss.theoj.org/papers/10.21105/joss.05035.pdfmachine learning interatomic potentials, neural networks, molecular dynamicstag:joss.theoj.org,2005:Paper/43582023-07-19T17:41:02Z2023-07-20T00:01:41ZPhysics-Informed Neural networks for Advanced modelingaccepted0.0.22023-03-21 14:04:01 UTC872023-07-19 17:41:02 UTC820235352DarioCosciaSISSA, International School of Advanced Studies, Via Bonomea 265, Trieste, Italy0000-0001-8833-6833AnnaIvagnesSISSA, International School of Advanced Studies, Via Bonomea 265, Trieste, Italy0000-0002-2369-4493NicolaDemoSISSA, International School of Advanced Studies, Via Bonomea 265, Trieste, Italy0000-0003-3107-9738GianluigiRozzaSISSA, International School of Advanced Studies, Via Bonomea 265, Trieste, Italy0000-0002-0810-881210.21105/joss.05352https://doi.org/10.5281/zenodo.8163732Pythonhttps://joss.theoj.org/papers/10.21105/joss.05352.pdfpython, deep learning, physics-informed neural networks, scientific machine learning, differential equations.tag:joss.theoj.org,2005:Paper/43152023-07-19T16:44:11Z2023-07-21T12:42:35ZNetgraph: Publication-quality Network Visualisations in Pythonaccepted4.12.42023-03-16 15:43:55 UTC872023-07-19 16:44:11 UTC820235372PaulJ. n.BrodersenDepartment of Pharmacology, University of Oxford, United Kingdom0000-0001-5216-786310.21105/joss.05372https://doi.org/10.5281/zenodo.8138403Pythonhttps://joss.theoj.org/papers/10.21105/joss.05372.pdfgraph, network, visualisation, visualization, matplotlib, networkx, igraph, graph-tooltag:joss.theoj.org,2005:Paper/39652023-07-05T12:56:44Z2023-07-06T00:01:11ZDeBEIR: A Python Package for Dense Bi-Encoder Information Retrievalacceptedv0.0.12022-10-17 06:11:59 UTC872023-07-05 12:56:44 UTC820235017VincentNguyenAustralian National University, School of Computing, Commonwealth Scientific and Industrial Research Organisation, Data610000-0003-1787-8090SarvnazKarimiCommonwealth Scientific and Industrial Research Organisation, Data610000-0002-4927-3937ZhenchangXingAustralian National University, School of Computing, Commonwealth Scientific and Industrial Research Organisation, Data610000-0001-7663-142110.21105/joss.05017https://doi.org/10.5281/zenodo.8103783Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05017.pdfinformation retrieval, dense retrieval, bi-encoder, transformers, pytorch, python, deep learning, neural networks, machine learning, natural language processingtag:joss.theoj.org,2005:Paper/41902023-05-17T14:31:41Z2023-05-18T00:01:24ZXGI: A Python package for higher-order interaction networksacceptedv0.5.42023-02-03 15:17:14 UTC852023-05-17 14:31:41 UTC820235162NicholasW.LandryVermont Complex Systems Center, University of Vermont, USA, Department of Mathematics and Statistics, University of Vermont, USA0000-0003-1270-4980MaximeLucasCENTAI Institute, Italy0000-0001-8087-2981IacopoIacopiniDepartment of Network and Data Science, Central European University, Austria0000-0001-8794-6410GiovanniPetriCENTAI Institute, Italy0000-0003-1847-5031AliceSchwarzeDepartment of Mathematics, Dartmouth College, USA0000-0002-9146-8068AlicePataniaVermont Complex Systems Center, University of Vermont, USA, Department of Mathematics and Statistics, University of Vermont, USA0000-0002-3047-4376LeoTorresMax Planck Institute for Mathematics in the Sciences, Germany0000-0002-2675-277510.21105/joss.05162https://doi.org/10.5281/zenodo.7939055Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05162.pdfpython, higher-order, hypergraph, simplicial complextag:joss.theoj.org,2005:Paper/41652023-05-15T14:10:03Z2023-05-16T00:01:36Zfractopo: A Python package for fracture network analysisacceptedv0.5.22023-01-16 08:40:25 UTC852023-05-15 14:10:03 UTC820235300NikolasOvaskainenGeological Survey of Finland, University of Turku, Finland0000-0003-1562-028010.21105/joss.05300https://doi.org/10.5281/zenodo.7915808Nix, Python, Luahttps://joss.theoj.org/papers/10.21105/joss.05300.pdfgeology, structural geology, fracture network, GIStag:joss.theoj.org,2005:Paper/39442023-05-12T12:14:29Z2023-05-13T00:01:21ZGraphNeT: Graph neural networks for neutrino telescope event reconstructionacceptedv0.2.22022-10-06 11:55:25 UTC852023-05-12 12:14:29 UTC820234971AndreasSøgaardNiels Bohr Institute, University of Copenhagen, Denmark0000-0002-0823-056XRasmusF.ØrsøeNiels Bohr Institute, University of Copenhagen, Denmark, Technical University of Munich, Germany0000-0001-8890-4124MortenHolmNiels Bohr Institute, University of Copenhagen, Denmark0000-0003-1383-2810LeonBozianuNiels Bohr Institute, University of Copenhagen, Denmark0000-0002-1243-9980AskeRostedChiba University, Japan0000-0003-2410-400XTroelsC.PetersenNiels Bohr Institute, University of Copenhagen, Denmark0000-0003-0221-3037KaareEndrupIversenNiels Bohr Institute, University of Copenhagen, Denmark0000-0001-6533-4085AndreasHermansenNiels Bohr Institute, University of Copenhagen, Denmark0009-0006-1162-9770TimGuggenmosTechnical University of Munich, GermanyPeterAndresenNiels Bohr Institute, University of Copenhagen, Denmark0009-0008-5759-0490MartinHaMinhTechnical University of Munich, Germany0000-0001-7776-4875LudwigNesteTechnical University of Dortmund, Germany0000-0002-4829-3469MoustHolmesNiels Bohr Institute, University of Copenhagen, Denmark0009-0000-8530-7041AxelPonténUppsala University, Sweden0009-0008-2463-2930KaylaLeonardDeHoltonPennsylvania State University, USA0000-0002-8795-0601PhilippEllerTechnical University of Munich, Germany0000-0001-6354-520910.21105/joss.04971https://doi.org/10.5281/zenodo.7928487Pythonhttps://joss.theoj.org/papers/10.21105/joss.04971.pdfmachine learning, deep learning, neural networks, graph neural networks, astrophysics, particle physics, neutrinostag:joss.theoj.org,2005:Paper/42762023-04-29T15:06:01Z2023-04-30T00:03:13Zgraphlayouts: Layout algorithms for network visualizations in Racceptedv0.8.42023-03-03 20:31:57 UTC842023-04-29 15:06:01 UTC820235238DavidSchochGESIS - Leibniz Institute for the Social Sciences0000-0003-2952-481210.21105/joss.05238https://doi.org/10.5281/zenodo.7870213R, C++https://joss.theoj.org/papers/10.21105/joss.05238.pdfnetwork visualization, graph drawing, layout algorithms