{"id":39084276,"url":"https://github.com/rise-unibas/networks_gephi","last_synced_at":"2026-01-17T18:35:07.411Z","repository":{"id":147016950,"uuid":"587304686","full_name":"RISE-UNIBAS/networks_gephi","owner":"RISE-UNIBAS","description":"RISE crash course: Gephi","archived":false,"fork":false,"pushed_at":"2025-03-14T13:16:21.000Z","size":1517,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-05T04:55:17.797Z","etag":null,"topics":["gephi","network-analysis"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RISE-UNIBAS.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-01-10T12:52:21.000Z","updated_at":"2025-03-14T13:16:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"9efbbacf-6942-4b9e-81bf-5e890bb39e68","html_url":"https://github.com/RISE-UNIBAS/networks_gephi","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/RISE-UNIBAS/networks_gephi","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RISE-UNIBAS%2Fnetworks_gephi","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RISE-UNIBAS%2Fnetworks_gephi/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RISE-UNIBAS%2Fnetworks_gephi/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RISE-UNIBAS%2Fnetworks_gephi/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RISE-UNIBAS","download_url":"https://codeload.github.com/RISE-UNIBAS/networks_gephi/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RISE-UNIBAS%2Fnetworks_gephi/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28516078,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-17T18:28:00.501Z","status":"ssl_error","status_checked_at":"2026-01-17T18:28:00.150Z","response_time":85,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["gephi","network-analysis"],"created_at":"2026-01-17T18:35:07.209Z","updated_at":"2026-01-17T18:35:07.391Z","avatar_url":"https://github.com/RISE-UNIBAS.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# **Network analysis in the Humanities. Gephi**\n\nIntroduction course to Network analysis and visualization with Gephi.\n\nBy José Luis Losada\n\n☞ Course outline\n\n- [Showcase](#showcase)\n- [Networks](#networks)\n- [Formalization and file formats](#formalization-and-file-formats)\n- [Metrics](#metrics)\n- [Tools](#tools)\n- [Data](#data-for-this-course)\n- [Hands-on](#step-by-step-instructions)\n- [Tutorials, manuals, references](#tutorials-manuals-references)\n\n# Network analysis in the Humanities\n\n## _Showcase_\n\n- Characters Networks:\n    - Co-occurrence in Drama: [Dracor](https://dracor.org)\n    - Co-occurrence in Narrative: [_Les Miserables_ (graph)](https://ouestware.gitlab.io/retina/1.0.0-beta.1/#/graph/?url=https%3A%2F%2Fraw.githubusercontent.com%2Fgephi%2Fgephi%2Ff27ae4fc88cd1c43820b31d94eaf7c6df57782d0%2Fmodules%2FWelcomeScreen%2Fsrc%2Fmain%2Fresources%2Forg%2Fgephi%2Fdesktop%2Fwelcome%2Fsamples%2FLes%2520Miserables.gexf\u0026r=v\u0026sa=r\u0026ca=m-s\u0026st=r); [_Les Miserables_ (matrix)](https://bost.ocks.org/mike/miserables/)\n    - Dynamic: [Visualising the dynamics of character networks](https://maladesimaginaires.github.io/intnetviz/)\n    - Paratexts: [Project Bieses](https://www.bieses.net/editorial.html), [corpus CECLE \u0026 CICLE](https://editio.github.io/grafos/paratextos/)\n- Textual Networks:\n    - [Stylometry on Drama](https://editio.github.io/grafos/teatro)\n- Historical Networks:\n    - [Mapping the archives of the League of Nations ICIC (1919-1927)](https://grandjeanmartin.github.io/intellectual-cooperation)\n    - [Letter exchange](https://ernie.uva.nl/viewer.p/21/59/scenario/75/geo/)\n- Spatial Networks:\n    - [Core vs periphery](https://editio.github.io/mapping.literature/spatialnet.html#persiles_core_vs_periphery)\n- Bibliographic networks:\n    - Citation: [Vosviewer](https://tinyurl.com/y36v4cb3)\n    - Content similarity: [Connected Papers](https://www.connectedpapers.com/main/3149a915f738f044778e3decdb4278e2bad17808/Gephi%3A-An-Open-Source-Software-for-Exploring-and-Manipulating-Networks/graph), [inciteful](https://inciteful.xyz)\n- Cultural networks:\n    - [Awards and winners](https://w.wiki/BwKC) \n- Semantic networks:\n    - [Word Families](https://www.rae.es/dhle/dhle_grafo.php?id=39279)\n- Author (ego) networks:\n    - [co-written drama](https://editio.github.io/presentations/imagenes/cola_moreto_colaboradas)      \n    \n# Networks\n\n|network |nodes|edges|\n|--|--|--|\n|Theater Plays|character|co-appearance on the scene|\n|Stylometry|plays|stylistic similarity|\n|Scientific collaboration|authors|co-authoring|\n|...|... |...|\n\n- Method of representing connection or interaction patterns between parts of a system.\n\n- The concept of network supposes a relational structure that can be studied (1) in a logical and mathematical way: Graph theory (discipline). History: [Euler and the seven bridges of Königsberg](https://medium.com/@satoshihgsn/seven-bridges-of-königsberg-can-this-diagram-be-drawn-in-a-single-stroke-e261980711a1).\n- (2) Exploration through visualization.\n\n\u003e “Networks are extraordinary calculating devices, but they are also maps, instruments of navigation and representation” (Jacomy 2017: 155). \n\n## Basic concepts. Nodes and edges\n\n- Network: points joined by lines.\n- points: _nodes_ or _vertices_.\n- lines: _edges_ o _links_.\n- Attributes: extra information about nodes or edges\n- Types of networks:\n    - Defined by the nodes: [bipartite](https://mathworld.wolfram.com/BipartiteGraph.html), [simple](https://mathworld.wolfram.com/SimpleGraph.html), [disconnected](https://mathworld.wolfram.com/DisconnectedGraph.html), ...\n    - Define by the edges: [multiple](https://mathworld.wolfram.com/Multigraph.html), [directed](https://mathworld.wolfram.com/DirectedGraph.html), ...\n\n## Simple Network\n\n![](images/terms_simple.png)\n\n## Bipartite Network\n\n![](images/terms_bipartita.png)\n \n## Multiple Network\n\n![](images/terms_multiple.png) \n\n## Multiple and Directed Network\n\n![](images/terms_multiple_directed.png) \n\n# Formalization and file formats\n\n## Formalization\n\nEdgelists, matrices, adjacency lists\n\n**Edgelist**: it is a set of structured data that contains at least two columns: a column of nodes that are the source of a connection (source) and another column of nodes that are the destination of the connection (target). The rest of the columns correspond to the attributes.\n\n|source |target|weight|lang|type|\n|-------|------|----|-----|----|\n|Juan|Elena |4 |esp     |undirected|\n|Juan|Hans  |2  |de     |undirected|\n|Juan|Marta  |1 |eng     |undirected|\n|Juan|Marek |1  |de     |undirected|\n|...|... |... |...|...|\n\n**[Adjacency matrix](https://mathworld.wolfram.com/AdjacencyMatrix.html)**: a square matrix (equal number of columns and rows)\n\n| |Juan|Hans|Elena|Marta|Marek|\n|--|--|------|----|-----|----|\n|**Juan**|0|1|1|1|1|\n|**Hans**|1|0|0|1|1|\n|**Elena**|1|0|0|0|0|\n|**Marta**|1|1|0|0|0|\n|**Marek**|1|1|0|0|0|\n\n## File Formats\n\n- ```CSV```. Edgelist in CSV:\n\n```\nsource,target,language,weight\nJuan,Elena,esp,4\nJuan,Hans,de,2\nJuan,Marta,eng,1\nJuan,Marek,de,1\nJuan,Marek,esp,1\nJuan,Marek,pol,5\nHans,Marta,eng,1\nHans,Marek,de,1\n```\n\n- ```CSV```. Edgelist + Nodes in CSV:\n\n```\nsource,target\n1,4\n1,2\n1,3\n\nid,Label\n1,Juan\n2,Hans\n3,Marta\n4,Elena\n```\nIt is recommended to save structured data in CSV, although Gephi accepts tables in Excel.\n\n- ```gexf``` (XML)\n\n```xml\n[...]\n      \u003cnode id=\"Marek\" label=\"Marek\"\u003e\n        \u003cattvalues\u003e\n          \u003cattvalue for=\"att1\" value=\"2.0\"/\u003e\n        \u003c/attvalues\u003e\n        \u003cviz:size value=\"4.0\"/\u003e\n        \u003cviz:position x=\"-22.013721\" y=\"26.080078\"/\u003e\n        \u003cviz:color r=\"255\" g=\"99\" b=\"71\"/\u003e\n      \u003c/node\u003e\n    \u003c/nodes\u003e\n    \u003cedges\u003e\n      \u003cedge id=\"0\" source=\"Juan\" target=\"Hans\" weight=\"2.0\"/\u003e\n      \u003cedge id=\"1\" source=\"Juan\" target=\"Elena\" weight=\"4.0\"/\u003e\n      \u003cedge id=\"2\" source=\"Juan\" target=\"Marta\"/\u003e\n      \u003cedge id=\"3\" source=\"Juan\" target=\"Marek\" weight=\"7.0\"/\u003e\n      \u003cedge id=\"4\" source=\"Hans\" target=\"Marta\"/\u003e\n      \u003cedge id=\"5\" source=\"Hans\" target=\"Marek\"/\u003e\n    \u003c/edges\u003e\n  \u003c/graph\u003e\n\u003c/gexf\u003e\n```\n\n- [More file formats](https://gephi.org/users/supported-graph-formats/) (supported by Gephi)\n\n# Visualization (_spatialization_)\n\nSame graph, different layout.\n\n![](images/network_viz.png)\n\nBipartite network\n\n![](images/terms_bipartita_layout.png)\n\n## Algorithms for drawing the graph\n\n![](images/network_layouts.png)\n\n- Common Gephi Algorithms: _Force Atlas_, _Fruchterman Reingold_,...\n\n# Metrics\n\n![](images/terms_metrics.png)\n\n- _Degree centrality_: nº of connections.\n- _Betweenness centrality_: bridge nodes. \n- _Eigenvector centrality_: nodes connected to well-connected nodes.\n- _Modularity_ (Louvain, Leiden algorithms): clusters of nodes.\n- ...\n\n![degree-distribution](images/degree-distribution.png)\n\n# Tools\n\nWorkflow: from data to visualization.\n\n![work flow](images/workflow.png)\n\n- Programming languages (full workflow): R, Python, JavaScript,...\n- OpenRefine, Table2net,...\n- Tableau, Nodegoat,...\n- Gephi, Cytoscape, VOSviewer, Graphext, orange,...\n\n## Gephi. Open Graph Viz Platform\n\nGephi has restarted its development in recent years.  It can be downloaded from its \u003chttps://gephi.org\u003e page or directly from the repository on github [gephi/releases](https://github.com/gephi/gephi/releases).\n\nOne of the advantages of the new versions (since 0.9.3) is that it already comes with Java (program language and execution environment for programs such as Gephi). More about the installation at \u003chttps://gephi.org/users/install/\u003e.\n\nNew in 2023! [Gephi Lite](https://gephi.org/gephi-lite/)\n\n## Interface: Panel _Overview_\n\n![](images/gephi_interfaz.png)\n\n## Plugins for Gephi:\n\nThey are located in ```Tools \u003e Plugin```. They add extra functionalities to Gephi (metrics, import, export, spatializations, ...).\n\n- _Multimode networks transformation_: it projects a bipartite network into a simple one.\n\n- _Sigma exporter_: it exports the graph to visualize it dynamically using javascript and html.\n\n- _Leiden algorithm_: Modularity algorithm.\n\n# Data for this course\n\nCSV and GEXF files are located in the folder ```/data``` in this repository\n\n## Theater\n\nCo-appearance character networks in theater. The source of the data is \u003chttps://dracor.org\u003e, from where they can be downloaded; I add them to ```/data``` just as back up copy.\n\n  - ```calderon_VidaEsSueno_ezlinavis.csv```\n  - ```span000014-valle-luces.gexf```\n\n## Literary awards\n\n35 literary awards and 1325 award-winning authors: data obtained from Wikidata. CSV table with 3 variables: prizes, winners and gender (masc./fem.); bipartite network and simple networks in GEXF format.\n\n- ```authors_and_awards.csv```\n- ```authors_and_awards.gexf```\n- ```authors.gexf```\n- ```awards.gexf```\n\nDataset (+ node and egdes lists) is available in [editio/premios-literarios](https://github.com/editio/premios-literarios) and Zenodo: José Luis Losada (2022) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6464417.svg)](https://doi.org/10.5281/zenodo.6464417)\n\n## Stylometry\n\nStylometry Network of plays of 17th. C. Spanish  Theater. The nodes represent plays linked according to their stylistic similarity. Analysis performed using the consensus tree (2000-5000 MFW) and Delta distance with the R package, stylo (Eder, Rybicki and Kestemont, 2016), on a corpus of circa 700 plays and 50 authors. Interactive visualization in: [Stylometry on Drama](https://editio.github.io/grafos/teatro)\n\n- ```stylometry_theater.gexf```\n\n## Bibliography\n\nCo-authoring network of 3500 publications on Stylometry. The bibliography has been compiled by Christof Schöch, _Bibliography on Stylometry_, 2017, DOI: [10.5281/zenodo.835190](https://doi.org/10.5281/zenodo.3629772).\n\n- ```biblio_stylo.gexf```\n\n## Correspondence\n\nCorrespondence network of Alexander von Humboldt (sample of 105 letters). Data obtained from [edition humboldt digital](https://edition-humboldt.de/index.xql?l=de) (CC BY-SA 4.0.) Sender, receiver, and date sent extracted from letters encoded in TEI.\n\n- ```humboldt_edgelist.csv```\n- ```humboldt_network.gexf```\n\n\n# Step-by-step instructions\n \n## Character networks\n\n☞ Practice the basics of an edgelist, how to load it into Gephi and perform the first steps of visualization and metrics.\n\n1. [Dracor](https://dracor.org) \u003e tools \u003e https://ezlinavis.dracor.org \u003e Examples \u003e Calderón de la Barca\u003e download _edge list_.\n2. Gephi \u003e File \u003e Import spreadsheet (CSV) \u003e next \u003e finish.\n- Layout: Fruchterman Reingold.\n- Nodes size based on _degree_: Appearance \u003e nodes \u003e size [icon circles ![](/images/size.png)] \u003e Ranking \u003e Choose an attribute \u003e Degree [min. 10 - max. 50].\n- Nodes labels: \"copy data to other column\" (_Data laboratory_). Alternative: \"select attributes to display as labels\" (_Overview_).\n- Centrality measures (Betweenness/Eigenvector): Segismundo vs Clarín (statistics \u003e Network Diameter; Eigenvector Centrality).\n\n☞ Familiarize with GEXF file format, open en Gephi, nodes attribute (male/female).\n\n1. [Dracor](https://dracor.org) \u003e corpora \u003e [Spanish Drama Corpus](https://dracor.org/span) \u003e Valle Inclán, _Luces de bohemia_ \u003e Downloads \u003e Archivo en gexf.\n2. Gephi \u003e open \u003e [no changes] \u003e ok.\n- Data exploration: _label_, _gender_ (_Data laboratory_).\n- Appearance \u003e nodes \u003e color [icon palette ![](/images/color.png)] \u003e Partition \u003e Choose an attribute \u003e gender\n- Layout: Force Atlas 2 [Prevent overlap, Disuade Hubs, Scaling = 40] \u003e run|stop.\n\n## From the data to the network: awards and winners\n\n☞ Transform structured data (CSV) into an edgelist (GEXF)\n\n1. ```/data``` \u003e ```authors_and_awards.csv```\n2. [table2net](https://medialab.github.io/table2net/) (transformation in the browser).\n3. Load table \u003e Type of Network \u003e Nodes \u003e Build the network \u003e Download.\n\n  - 3.1 Network type: bipartite.\n  - 3.2 Nodes 1: authors | attribute: masc/fem.\n  - 3.3 Nodos 2: awards.\n\n## Awards and winners network (1)\n\n☞ Explore bipartite networks.\n\n1. Gephi \u003e open ```authors_and_awards.gexf```.\n- Layout: Force Atlas 2 \u003e run|stop; \u003e Prevent overlap \u003e run|stop; Zoom\n- Appearance \u003e nodes \u003e color [icon palette ![](/images/color.png)] \u003e Partition \u003e Choose an attribute \u003e Type\n- Appearance \u003e nodes \u003e size [icon circles ![](/images/size.png)] \u003e Ranking \u003e Choose an attribute \u003e Degree [min. 10 - max. 50] (number of authors by award).\n- Nodes Labels: Show node Labels; More settings \u003e Labels \u003e Hide non-selected. \n- [reset colors] \u003e Appearance \u003e nodes \u003e color [icon palette ![](/images/color.png)] \u003e Partition \u003e Choose an attribute \u003e gender.\n\n## Awards and winners network (2)\n\n☞ Explore simple networks\n\nFiles are available in ```/data/awards.gexf```; ```/data/authors.gexf```. They can also be created from the structured data (CSV) with ([table2net](https://medialab.github.io/table2net/)) o using a transformation from the bipartite network (☞ _vide infra_).\n\n1. Gephi \u003e open ```awards.gexf```\n  - Layout: Force atlas 2 [Prevent overlap, Disuade Hubs, Scaling = 50]\n  - Appearance \u003e nodes \u003e size [icon circles ![](/images/size.png)] \u003e Ranking \u003e Choose an attribute \u003e Degree [min. 5 - max. 30].\n  - Modularity: Community detection \u003e Modularity \u003e run.\n  - Appearance \u003e nodes \u003e color [icon palette ![](/images/color.png)] \u003e Partition \u003e Choose an attribute \u003e Modularity Class.\n\n  - Check centrality metrics:\n      - Statistics \u003e eigenvector Centrality.\n      - Appearance \u003e nodes \u003e size [icon circles ![](/images/size.png)] \u003e Ranking \u003e Choose an attribute \u003e eigenvector Centrality.\n\n2. Gephi \u003e open ```authors.gexf```\n  - Layout: Layout: Fruchterman Reingold.\n  - Appearance \u003e nodes \u003e color [icon palette ![](/images/color.png)] \u003e Partition \u003e Choose an attribute \u003e sexlabel.\n  - Appearance \u003e nodes \u003e size [icon circles ![](/images/size.png)] \u003e Ranking \u003e Choose an attribute \u003e Degree [min. 5 - max. 30].\n\n☞ Switching from one type of network to another (projection).\n\n1. Plugin: multimodal networks transformation.\n  \n  - Bipartite Network.\n  - Load attributes \u003e type:\n    - Award \u003e Author / Author \u003e Award (Simple network of awards)\n    - Author \u003e Award / Award \u003e Author (Simple network of authors)\n  - Remove nodes, edges.\n  - Run.\n\n## Stylometry\n\n☞ Explore textual networks\n\n1. Gephi \u003e open ```stylometry_theater.gexf```.\n- Layout: Force atlas 2 [Prevent overlap, Disuade Hubs, Scaling = 200].\n- Appearance \u003e nodes \u003e color [icon palette ![](/images/color.png)] \u003e Partition \u003e Choose an attribute \u003e Classes (autores) \u003e Palette \u003e Generate [Limit number of colors: unchecked] \u003e generate.\n- Appearance \u003e nodes \u003e size [icon circles ![](/images/size.png)] \u003e Unique \u003e size = 20.\n- Nodes Labels: Show node Labels; More settings \u003e Labels \u003e Hide non-selected.  \n\nCompare with modularity algorithms:\n\n- Modularity: Community detection \u003e Modularity \u003e run.\n- Appearance \u003e nodes \u003e color [icon palette ![](/images/color.png)] \u003e Partition \u003e Choose an attribute \u003e Modularity Class.\n\n## Bibliography\n\n☞ Explore disconnected networks\n\n1. Gephi \u003e open ```biblio_stylo.gexf```.\n- Layout: Fruchterman Reingold (compare with Force Atlas 2).\n- Compare with modularity algorithms.\n\n## Correspondece\n\n☞ Explore directed networks, Gephi's limits with multiple edges, filters and timelines.\n\n1. Gephi \u003e File \u003e Import spreadsheet (CSV) \u003e next \u003e Time representation [Intervals] \u003e Finish \u003e Edges merge strategy **[Don't merge]**\n\n- Layout: Fruchterman Reingold\n- Nodes labels: \"copy data to other column\" (_Data laboratory_) to allow for searching (cmd/ctrl F); (_Overview_): labels \"Hide non-selected\"; (_Overview_): edges \"Selection color checked\" (in-out).\n\n2. (_Data laboratory_) multiple edges? Humboldt -\u003e Ehrenberg\n\n![](images/humboldt_multiple-weighted.png)\n\n3. Gephi \u003e File \u003e Import spreadsheet (CSV) [...] Finish \u003e Edges merge strategy **[merge]** \u003e New workspace.\n\n4. Filters (see [Using filters in Gephi](https://seinecle.github.io/gephi-tutorials/generated-html/using-filters-en.html))\n\n- Filters \u003e Edges \u003e Mutual Edges \u003e Filter\n- Filters \u003e Topology \u003e In Degree | Out Degree \u003e Filter \n\n5. Timeline\n\n- Use the network with multiple edges (be aware of the limitations also for the timeline)\n\n- (_Data laboratory_) Merge columns \u003e date_sent \u003e columns to merge \u003e merge strategy \u003e Create time interval \u003e Parse dates\n- Enable timeline \u003e Set time format (bottom left) [date format]  \u003e Set play settings (bottom left) [one bound].\n\n## Out of Gephi: Publication possibilities\n\n☞ Static and dynamic forms of graph representation outside Gephi\n\n1. Panel _Overview_: Screeshot (left), More settings (right)...\n2. Panel _Preview_: export SVG, PNG, PDF.\n3. Plugin: _Sigma Exporter_. It creates a folder with the required libraries, data and files to display the graph interactively in a browser. It is necessary to upload it to a web server, for example, using [Github Pages](https://pages.github.com). For testing purposes, It is possible to launch a local server: [Instructions](http://phc.uni.wroc.pl/interreg/w/losada/trans.html#web-server-in-your-computer).\n4. [Retina](https://ouestware.gitlab.io/retina/1.0.0-beta.1/) (Web app, beta): \nVisualization in the browser (offline / online) from a GEXF file.\n5. [Cosmograph](https://cosmograph.app/): Visualization in the browser from a .csv file, also timelines. \n\n\n# Tutorials, manuals, references\n\n- Albert-László Barabási, [Network Science](http://networksciencebook.com), 2016.\n- Mathieu Bastian, Sebastien Heymann, Mathieu Jacomy, “Gephi: An Open Source Software for Exploring and Manipulating Networks”, _International AAAI Conference on Weblogs and Social Media_, 2009, pp. 361-362.\n- Gephi, [Learn how to use Gephi](https://gephi.org/users/).\n- Martin Grandjean, [Gephi: Introduction to Network Analysis and Visualization](http://www.martingrandjean.ch/gephi-introduction), 14/10/2015.\n- Mathieu Jacomy, “A standard for presenting network visualizations”, _Reticular_, 01/03/2019, \u003chttps://reticular.hypotheses.org/834\u003e.\n- Mathieu Jacomy, Venturini, Tommaso, Liliana Bounegru, and Jonathan Gray (2017). “How to Tell Stories with Networks: Exploring the Narrative Affordances of Graphs with the Iliad”. In The Datafied Society, edited by Mirko Tobias Schäfer and Karin van Es, 155–170. Amsterdam. \u003chttps://doi.org/10.1515/9789048531011-014\u003e\n- Clément Levallois, [Gephi tutorials](https://seinecle.github.io/gephi-tutorials/), Last update: 2022.\n- Mark Newman, _Networks: An Introduction_, Oxford University Press, 2010.\n- Katherine Ognyanova, [Static and dynamic network visualization with R](https://kateto.net/network-visualization), 2021\n- Katharina A. Zweig, _Network Analysis Literacy: A Practical Approach to the Analysis of Networks_, Springer, 2016.\n\n## License\n\n\u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"\u003e\u003cimg alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /\u003e\u003c/a\u003e\u003cbr /\u003eThis work is licensed under a \u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"\u003eCreative Commons Attribution 4.0 International License\u003c/a\u003e.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frise-unibas%2Fnetworks_gephi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frise-unibas%2Fnetworks_gephi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frise-unibas%2Fnetworks_gephi/lists"}