diff --git a/joss.06016/10.21105.joss.06016.crossref.xml b/joss.06016/10.21105.joss.06016.crossref.xml new file mode 100644 index 0000000000..6debc9b18d --- /dev/null +++ b/joss.06016/10.21105.joss.06016.crossref.xml @@ -0,0 +1,412 @@ + + + + 20240309T213717-c4c182635a393f940f7adf8b7bd1acb3efcdb6a3 + 20240309213717 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 03 + 2024 + + + 9 + + 95 + + + + HyperNetX: A Python package for modeling complex +network data as hypergraphs + + + + Brenda + Praggastis + https://orcid.org/0000-0003-1344-0497 + + + Sinan + Aksoy + https://orcid.org/0000-0002-3466-3334 + + + Dustin + Arendt + https://orcid.org/0000-0003-2466-199X + + + Mark + Bonicillo + https://orcid.org/0009-0003-9764-2180 + + + Cliff + Joslyn + https://orcid.org/0000-0002-5923-5547 + + + Emilie + Purvine + https://orcid.org/0000-0003-2069-5594 + + + Madelyn + Shapiro + https://orcid.org/0000-0002-2786-7056 + + + Ji Young + Yun + + + + 03 + 09 + 2024 + + + 6016 + + + 10.21105/joss.06016 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10795225 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6016 + + + + 10.21105/joss.06016 + https://joss.theoj.org/papers/10.21105/joss.06016 + + + https://joss.theoj.org/papers/10.21105/joss.06016.pdf + + + + + + Hypergraph analytics of domain name system +relationships + Joslyn + Algorithms and models for the web +graph + 10.1007/978-3-030-48478-1_1 + 978-3-030-48478-1 + 2020 + Joslyn, C. A., Aksoy, S., Arendt, D., +Firoz, J., Jenkins, L., Praggastis, B., Purvine, E., & Zalewski, M. +(2020). Hypergraph analytics of domain name system relationships. In B. +Kamiński, P. Prałat, & P. Szufel (Eds.), Algorithms and models for +the web graph (pp. 1–15). Springer International Publishing. +https://doi.org/10.1007/978-3-030-48478-1_1 + + + The application of directed hyper-graphs for +analysis of models of information systems + Molnár + Mathematics + 5 + 10 + 10.3390/math10050759 + 2022 + Molnár, B., & Benczúr, A. (2022). +The application of directed hyper-graphs for analysis of models of +information systems. Mathematics, 10(5), 759. +https://doi.org/10.3390/math10050759 + + + Hypernetwork science via high-order +hypergraph walks + Aksoy + EPJ Data Science + 1 + 9 + 10.1140/epjds/s13688-020-00231-0 + 2020 + Aksoy, S. G., Joslyn, C., Marrero, C. +O., Praggastis, B., & Purvine, E. (2020). Hypernetwork science via +high-order hypergraph walks. EPJ Data Science, 9(1), 16. +https://doi.org/10.1140/epjds/s13688-020-00231-0 + + + Graphs and Hypergraphs,(translated by Edward +Minieka) + Berge + 10.1016/s0924-6509(09)x7013-3 + 1973 + Berge, C. (1973). Graphs and +Hypergraphs,(translated by Edward Minieka). American Elsevier, New York. +https://doi.org/10.1016/s0924-6509(09)x7013-3 + + + Hypergraph models of biological networks to +identify genes critical to pathogenic viral response + Feng + BMC bioinformatics + 1 + 22 + 10.1186/s12859-021-04197-2 + 2021 + Feng, S., Heath, E., Jefferson, B., +Joslyn, C., Kvinge, H., Mitchell, H. D., Praggastis, B., Eisfeld, A. J., +Sims, A. C., Thackray, L. B., & others. (2021). Hypergraph models of +biological networks to identify genes critical to pathogenic viral +response. BMC Bioinformatics, 22(1), 1–21. +https://doi.org/10.1186/s12859-021-04197-2 + + + Growth principles of natural +hypergraphs + Vazquez + arXiv preprint +arXiv:2208.03103 + 10.48550/arXiv.2208.03103 + 2022 + Vazquez, A. (2022). Growth principles +of natural hypergraphs. arXiv Preprint arXiv:2208.03103. +https://doi.org/10.48550/arXiv.2208.03103 + + + Hypergraphx: A library for higher-order +network analysis + Lotito + Journal of Complex Networks + 3 + 11 + 10.1093/comnet/cnad019 + 2023 + Lotito, Q. F., Contisciani, M., De +Bacco, C., Di Gaetano, L., Gallo, L., Montresor, A., Musciotto, F., +Ruggeri, N., & Battiston, F. (2023). Hypergraphx: A library for +higher-order network analysis. Journal of Complex Networks, 11(3), +cnad019. https://doi.org/10.1093/comnet/cnad019 + + + The SVD of convolutional weights: A CNN +interpretability framework + Praggastis + arXiv preprint +arXiv:2208.06894 + 10.48550/arXiv.2208.06894 + 2022 + Praggastis, B., Brown, D., Marrero, +C. O., Purvine, E., Shapiro, M., & Wang, B. (2022). The SVD of +convolutional weights: A CNN interpretability framework. arXiv Preprint +arXiv:2208.06894. +https://doi.org/10.48550/arXiv.2208.06894 + + + Hypernetwork science: From multidimensional +networks to computational topology + Joslyn + Unifying themes in complex systems +x + 10.1007/978-3-030-67318-5_25 + 978-3-030-67318-5 + 2021 + Joslyn, C. A., Aksoy, S. G., +Callahan, T. J., Hunter, L. E., Jefferson, B., Praggastis, B., Purvine, +E., & Tripodi, I. J. (2021). Hypernetwork science: From +multidimensional networks to computational topology. In D. Braha, M. A. +M. de Aguiar, C. Gershenson, A. J. Morales, L. Kaufman, E. N. Naumova, +A. A. Minai, & Y. Bar-Yam (Eds.), Unifying themes in complex systems +x (pp. 377–392). Springer International Publishing. +https://doi.org/10.1007/978-3-030-67318-5_25 + + + The why, how, and when of representations for +complex systems + Torres + SIAM Review + 3 + 63 + 10.1137/20M1355896 + 2021 + Torres, L., Blevins, A. S., Bassett, +D., & Eliassi-Rad, T. (2021). The why, how, and when of +representations for complex systems. SIAM Review, 63(3), 435–485. +https://doi.org/10.1137/20M1355896 + + + Phoenix: A scalable streaming hypergraph +analysis framework + Kurte + 10.1007/978-3-030-71704-9_1 + 2021 + Kurte, K., Imam, N., Hasan, S. M. S., +& Kannan, R. (2021). Phoenix: A scalable streaming hypergraph +analysis framework. +https://doi.org/10.1007/978-3-030-71704-9_1 + + + The stanford GraphBase: A platform for +combinatorial computing + Knuth + 1 + 1993 + Knuth, D. E. (1993). The stanford +GraphBase: A platform for combinatorial computing (Vol. 1). AcM Press +New York. + + + igraph enables fast and robust network +analysis across programming languages + Antonov + 2023 + Antonov, M., Csárdi, G., Horvát, S., +Müller, K., Nepusz, T., Noom, D., Salmon, M., Traag, V., Welles, B. F., +& Zanini, F. (2023). igraph enables fast and robust network analysis +across programming languages. +https://arxiv.org/abs/2311.10260 + + + The igraph software package for complex +network research + Csardi + InterJournal, complex systems + 5 + 1695 + 2006 + Csardi, G., Nepusz, T., & others. +(2006). The igraph software package for complex network research. +InterJournal, Complex Systems, 1695(5), 1–9. + + + HyperThesis: Topological hypothesis +management in a hypergraph knowledgebase. + Joslyn + TAC + 2018 + Joslyn, C. A., Robinson, M., Smart, +J., Agarwal, K., Bridgeland, D., Brown, A., Choudhury, S., Jefferson, B. +A., Praggastis, B., Purvine, E., & others. (2018). HyperThesis: +Topological hypothesis management in a hypergraph knowledgebase. +TAC. + + + XGI: A python package for higher-order +interaction networks + Landry + Journal of Open Source +Software + 85 + 8 + 10.21105/joss.05162 + 2023 + Landry, N. W., Lucas, M., Iacopini, +I., Petri, G., Schwarze, A., Patania, A., & Torres, L. (2023). XGI: +A python package for higher-order interaction networks. Journal of Open +Source Software, 8(85), 5162. +https://doi.org/10.21105/joss.05162 + + + SimpleHypergraphs. Jl—novel software +framework for modelling and analysis of hypergraphs + Antelmi + Algorithms and models for the web graph: 16th +international workshop, WAW 2019, brisbane, QLD, australia, july 6–7, +2019, proceedings 16 + 10.1007/978-3-030-25070-6_9 + 2019 + Antelmi, A., Cordasco, G., Kamiński, +B., Prałat, P., Scarano, V., Spagnuolo, C., & Szufel, P. (2019). +SimpleHypergraphs. Jl—novel software framework for modelling and +analysis of hypergraphs. Algorithms and Models for the Web Graph: 16th +International Workshop, WAW 2019, Brisbane, QLD, Australia, July 6–7, +2019, Proceedings 16, 115–129. +https://doi.org/10.1007/978-3-030-25070-6_9 + + + Introducing molecular hypernetworks for +discovery in multidimensional metabolomics data + Colby + bioRxiv + 10.1101/2023.09.29.560191 + 2023 + Colby, S. M., Shapiro, M. R., Lin, +A., Bilbao, A., Broeckling, C. D., Purvine, E., & Joslyn, C. A. +(2023). Introducing molecular hypernetworks for discovery in +multidimensional metabolomics data. bioRxiv. +https://doi.org/10.1101/2023.09.29.560191 + + + pandas-dev/pandas: Pandas + The pandas development team + 10.5281/zenodo.3509134 + 2020 + The pandas development team. (2020). +pandas-dev/pandas: Pandas (latest). Zenodo. +https://doi.org/10.5281/zenodo.3509134 + + + Data Structures for Statistical Computing in +Python + McKinney + Proceedings of the 9th Python in Science +Conference + 10.25080/Majora-92bf1922-00a + 2010 + McKinney, Wes. (2010). Data +Structures for Statistical Computing in Python. In Stéfan van der Walt +& Jarrod Millman (Eds.), Proceedings of the 9th Python in Science +Conference (pp. 56–61). +https://doi.org/10.25080/Majora-92bf1922-00a + + + Exploring network structure, dynamics, and +function using NetworkX + Hagberg + Proceedings of the 7th python in science +conference + 2008 + Hagberg, A. A., Schult, D. A., & +Swart, P. J. (2008). Exploring network structure, dynamics, and function +using NetworkX. In G. Varoquaux, T. Vaught, & J. Millman (Eds.), +Proceedings of the 7th python in science conference (pp. +11–15). + + + + + + diff --git a/joss.06016/10.21105.joss.06016.jats b/joss.06016/10.21105.joss.06016.jats new file mode 100644 index 0000000000..eef084f465 --- /dev/null +++ b/joss.06016/10.21105.joss.06016.jats @@ -0,0 +1,690 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6016 +10.21105/joss.06016 + +HyperNetX: A Python package for modeling complex network +data as hypergraphs + + + +https://orcid.org/0000-0003-1344-0497 + +Praggastis +Brenda + + + + +https://orcid.org/0000-0002-3466-3334 + +Aksoy +Sinan + + + + +https://orcid.org/0000-0003-2466-199X + +Arendt +Dustin + + + + +https://orcid.org/0009-0003-9764-2180 + +Bonicillo +Mark + + + + +https://orcid.org/0000-0002-5923-5547 + +Joslyn +Cliff + + + + +https://orcid.org/0000-0003-2069-5594 + +Purvine +Emilie + + + + +https://orcid.org/0000-0002-2786-7056 + +Shapiro +Madelyn + + + + + +Yun +Ji Young + + + + + +Pacific Northwest National Laboratory, USA + + + + +21 +6 +2023 + +9 +95 +6016 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +hypergraph +network science +simplicial-complexes +knowledge graph +simplicial-homology +s-linegraph +property hypergraph + + + + + + Summary +

HyperNetX (HNX) is an open source Python library for the analysis + and visualization of complex network data modeled as hypergraphs. + Initially released in 2019, HNX facilitates exploratory data analysis + of complex networks using algebraic topology, combinatorics, and + generalized hypergraph and graph theoretical methods on structured + data inputs. With its 2023 release, the library supports attaching + metadata, numerical and categorical, to nodes (vertices) and + hyperedges, as well as to node-hyperedge pairings (incidences). HNX + has a customizable Matplotlib-based visualization module as well as + HypernetX-Widget, its JavaScript addon for interactive exploration and + visualization of hypergraphs within Jupyter Notebooks. Both packages + are available on GitHub and PyPI. With a growing community of users + and collaborators, HNX has become a preeminent tool for hypergraph + analysis.

+ +

HNX-Widget is an add-on for the Jupyter Notebook + computational environment, enabling users to view and interactively + explore hypergraphs. The main features of the tool are: 1) + adjustable layout 2) advanced selection and 3) visual encoding of + node and edge properties. Metadata may be attached to the tool by + providing tabular data via two optional data frames indexed by node + and hyperedge identifiers. Above is an HNX-Widget visualization of a + Scene to Character mapping from the LesMis dataset + (Knuth, + 1993).

+ + + + + Statement of need +

For more than a century, graph theory has provided powerful methods + for studying network relationships among abstract entities. Since the + early 2000’s, software packages such as NetworkX + (Hagberg + et al., 2008) and igraph + (Antonov + et al., 2023; + Csardi + et al., 2006) have made these theoretical tools available to + data scientists for studying large data sets. Graphs represent + pairwise interactions between entities, but for many network datasets + this is a severe limitation. In 1973, hypergraphs were introduced by + Claude Berge + (Berge, + 1973) as a strict generalization of graphs: a hyperedge in a + hypergraph can contain any number of nodes, including 1, 2, or more. + Hypergraphs have been used to model complex network datasets in areas + such as the biological sciences, information systems, the power grid, + and cyber security. Hypergraphs strictly generalize graphs (all graphs + are (2-uniform) hypergraphs), and thus can represent additional data + complexity and have more mathematical properties to exploit (for + example, hyperedges can be contained in other hyperedges). As + mathematical set systems, simplicial and homological methods from + Algebraic Topology are well suited to aid in their analysis + (Cliff + A. Joslyn et al., 2021; + Torres + et al., 2021). With the development of hypergraph modeling + methods, new software was required to support experimentation and + exploration, which prompted the development of HyperNetX.

+ + Related Software +

Due to the diversity of hypergraph modeling applications, + hypergraph software libraries are often bootstrapped using data + structures and methods most appropriate to their usage. In 2020 + SimpleHypergraph.jl was made available for high performance + computing on hypergraphs using Julia. The library offers a suite of + tools for centrality analysis and community detection and integrates + its own visualization tools with those offered by HNX + (Antelmi + et al., 2019). In 2021 CompleX Group Interactions (XGI) was + released. Originally developed to efficiently discover spreading + processes in complex social systems, the library now offers a + statistics package as well as a full suite of hypergraph analysis + and visualization tools + (Landry + et al., 2023). More recently, in 2023 HyperGraphX (HGX) was + released, again with a full suite of tools for community detection + as well as general hypergraph analytics + (Lotito + et al., 2023). A nice compendium of many of the hypergraph + libraries created in the last decade can be found in Kurte et al. + (2021).

+

HNX leads the effort to share library capabilities by specifying + a Hypergraph Interchange Format (HIF) for storing hypergraph data as + a JSON object. Since hypergraphs can store metadata on its nodes, + hyperedges, and incidence pairs, a standardized format makes it easy + to share hypergraphs across libraries.

+ +

Visualizations from hypergraph libraries based on the + bipartite graph seen in grey under the HyperNetX visualization + (left side): XGI (Center), Landry et al. + (2023) + and SimpleHypergraph (Right), Antelmi et al. + (2019).

+ + + + + + Overview of HNX +

HNX serves as a platform for the collaboration and sharing of + hypergraph methods within the research community. Originally intended + to generalize many of the methods from NetworkX to hypergraphs, HNX + now has implementations for many hypergraph-specific metrics. While + graph paths can be measured by length, hypergraph paths also have a + width parameter s, given by the minimum intersection + size of incident hyperedges in the path + (Aksoy + et al., 2020). HNX uses this s parameter in + many of its core methods as well as in its + s-centrality module. As set systems, hypergraphs can + be viewed as subsets of abstract simplicial complexes – combinatorial + projections of geometric objects constructed from points, line + segments, triangles, tetrahedrons, and their higher dimensional + analogues. HNX’s Simplicial Homology module identifies and computes + the voids of different dimensions in the simplicial + complexes generated by modestly sized hypergraphs. These objects, + which are used for defining the Homology Groups + studied by Algebraic Topologists, offer new metrics for exploratory + data science.

+

As a collaborative platform, HNX contains contributed modules and + tutorials in the form of Jupyter notebooks for Laplacian clustering, + clustering and modularity, synthetic generation of hypergraphs, and + Contagion Theory. In its latest release, HNX 2.0 uses Pandas + dataframes + (McKinney, + 2010; + The + pandas development team, 2020) as its underlying data + structure, making the nodes and hyperedges of a hypergraph as + accessible as the cells in a dataframe. This simple design allows HNX + to import data from semantically loaded graphs such as property graphs + and knowledge graphs, in order to model and explore their higher order + relationships. Because it is open source, HNX provides a unique + opportunity for hypergraph researchers to implement their own methods + built from HNX and contribute them as modules and Jupyter tutorials to + the HNX user community.

+ + Projects using HNX +

HNX was created by the Pacific Northwest National Laboratory. It + has provided data analysis and visualization support for academic + papers in subject areas such as biological systems + (Colby + et al., 2023; + Feng + et al., 2021), cyber security + (Cliff + A. Joslyn et al., 2020), information systems + (Molnár + & Benczúr, 2022), neural networks + (Praggastis + et al., 2022), knowledge graphs + (Cliff + A. Joslyn et al., 2018), and the foundations of hypergraph + theory + (Vazquez, + 2022).

+ + + + + + + + + JoslynCliff A. + AksoySinan + ArendtDustin + FirozJesun + JenkinsLouis + PraggastisBrenda + PurvineEmilie + ZalewskiMarcin + + Hypergraph analytics of domain name system relationships + Algorithms and models for the web graph + + KamińskiBogumił + PrałatPaweł + SzufelPrzemysław + + Springer International Publishing + Cham + 2020 + 978-3-030-48478-1 + 10.1007/978-3-030-48478-1_1 + 1 + 15 + + + + + + MolnárBálint + BenczúrAndrás + + The application of directed hyper-graphs for analysis of models of information systems + Mathematics + MDPI + 2022 + 10 + 5 + 10.3390/math10050759 + 759 + + + + + + + AksoySinan G + JoslynCliff + MarreroCarlos Ortiz + PraggastisBrenda + PurvineEmilie + + Hypernetwork science via high-order hypergraph walks + EPJ Data Science + Springer Berlin Heidelberg + 2020 + 9 + 1 + 10.1140/epjds/s13688-020-00231-0 + 16 + + + + + + + BergeClaude + + Graphs and Hypergraphs,(translated by Edward Minieka) + American Elsevier, New York + 1973 + 10.1016/s0924-6509(09)x7013-3 + + + + + + FengSong + HeathEmily + JeffersonBrett + JoslynCliff + KvingeHenry + MitchellHugh D + PraggastisBrenda + EisfeldAmie J + SimsAmy C + ThackrayLarissa B + others + + Hypergraph models of biological networks to identify genes critical to pathogenic viral response + BMC bioinformatics + BioMed Central + 2021 + 22 + 1 + 10.1186/s12859-021-04197-2 + 1 + 21 + + + + + + VazquezAlexei + + Growth principles of natural hypergraphs + arXiv preprint arXiv:2208.03103 + 2022 + 10.48550/arXiv.2208.03103 + + + + + + LotitoQuintino Francesco + ContiscianiMartina + De BaccoCaterina + Di GaetanoLeonardo + GalloLuca + MontresorAlberto + MusciottoFederico + RuggeriNicolò + BattistonFederico + + Hypergraphx: A library for higher-order network analysis + Journal of Complex Networks + Oxford University Press + 2023 + 11 + 3 + 10.1093/comnet/cnad019 + cnad019 + + + + + + + PraggastisBrenda + BrownDavis + MarreroCarlos Ortiz + PurvineEmilie + ShapiroMadelyn + WangBei + + The SVD of convolutional weights: A CNN interpretability framework + arXiv preprint arXiv:2208.06894 + 2022 + 10.48550/arXiv.2208.06894 + + + + + + JoslynCliff A. + AksoySinan G. + CallahanTiffany J. + HunterLawrence E. + JeffersonBrett + PraggastisBrenda + PurvineEmilie + TripodiIgnacio J. + + Hypernetwork science: From multidimensional networks to computational topology + Unifying themes in complex systems x + + BrahaDan + AguiarMarcus A. M. de + GershensonCarlos + MoralesAlfredo J. + KaufmanLes + NaumovaElena N. + MinaiAli A. + Bar-YamYaneer + + Springer International Publishing + Cham + 2021 + 978-3-030-67318-5 + 10.1007/978-3-030-67318-5_25 + 377 + 392 + + + + + + TorresLeo + BlevinsAnn S. + BassettDanielle + Eliassi-RadTina + + The why, how, and when of representations for complex systems + SIAM Review + 2021 + 63 + 3 + https://doi.org/10.1137/20M1355896 + 10.1137/20M1355896 + 435 + 485 + + + + + + KurteKuldeep + ImamNeena + HasanS M Shamimul + KannanRamakrishnan + + Phoenix: A scalable streaming hypergraph analysis framework + 202110 + https://www.osti.gov/biblio/1830117 + 10.1007/978-3-030-71704-9_1 + + + + + + KnuthDonald Ervin + + The stanford GraphBase: A platform for combinatorial computing + AcM Press New York + 1993 + 1 + + + + + + AntonovMichael + CsárdiGábor + HorvátSzabolcs + MüllerKirill + NepuszTamás + NoomDaniel + SalmonMaëlle + TraagVincent + WellesBrooke Foucault + ZaniniFabio + + igraph enables fast and robust network analysis across programming languages + 2023 + https://arxiv.org/abs/2311.10260 + + + + + + CsardiGabor + NepuszTamas + others + + The igraph software package for complex network research + InterJournal, complex systems + 2006 + 1695 + 5 + 1 + 9 + + + + + + JoslynCliff A + RobinsonMichael + SmartJ + AgarwalKhushbu + BridgelandDavid + BrownAdam + ChoudhurySutanay + JeffersonBrett A + PraggastisBrenda + PurvineEmilie + others + + HyperThesis: Topological hypothesis management in a hypergraph knowledgebase. + TAC + 2018 + + + + + + LandryNicholas W. + LucasMaxime + IacopiniIacopo + PetriGiovanni + SchwarzeAlice + PataniaAlice + TorresLeo + + XGI: A python package for higher-order interaction networks + Journal of Open Source Software + The Open Journal + 2023 + 8 + 85 + https://doi.org/10.21105/joss.05162 + 10.21105/joss.05162 + 5162 + + + + + + + AntelmiAlessia + CordascoGennaro + KamińskiBogumił + PrałatPaweł + ScaranoVittorio + SpagnuoloCarmine + SzufelPrzemyslaw + + SimpleHypergraphs. Jl—novel software framework for modelling and analysis of hypergraphs + Algorithms and models for the web graph: 16th international workshop, WAW 2019, brisbane, QLD, australia, july 6–7, 2019, proceedings 16 + Springer + 2019 + 10.1007/978-3-030-25070-6_9 + 115 + 129 + + + + + + ColbySean M + ShapiroMadelyn R + LinAndy + BilbaoAivett + BroecklingCorey D + PurvineEmilie + JoslynCliff A + + Introducing molecular hypernetworks for discovery in multidimensional metabolomics data + bioRxiv + Cold Spring Harbor Laboratory + 2023 + https://www.biorxiv.org/content/early/2023/10/02/2023.09.29.560191 + 10.1101/2023.09.29.560191 + + + + + + The pandas development team + + pandas-dev/pandas: Pandas + Zenodo + 202002 + https://doi.org/10.5281/zenodo.3509134 + 10.5281/zenodo.3509134 + + + + + + McKinney + + Data Structures for Statistical Computing in Python + Proceedings of the 9th Python in Science Conference + + Walt + Millman + + 2010 + 10.25080/Majora-92bf1922-00a + 56 + 61 + + + + + + HagbergAric A. + SchultDaniel A. + SwartPieter J. + + Exploring network structure, dynamics, and function using NetworkX + Proceedings of the 7th python in science conference + + VaroquauxGaël + VaughtTravis + MillmanJarrod + + Pasadena, CA USA + 2008 + 11 + 15 + + + + +
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