{"id":21000453,"url":"https://github.com/matteofasulo/paris-euler","last_synced_at":"2026-04-06T21:30:57.495Z","repository":{"id":148130193,"uuid":"493171825","full_name":"MatteoFasulo/Paris-Euler","owner":"MatteoFasulo","description":"Euler Team - Social Network Analysis of Paris Transportation ","archived":false,"fork":false,"pushed_at":"2024-06-24T15:04:51.000Z","size":58630,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-31T11:59:54.737Z","etag":null,"topics":["data-science","jupyter-notebook","map","matplotlib","network-analysis","networkx","openstreetmap","pandas","python","social-network-analysis","transport"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MatteoFasulo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-05-17T08:56:55.000Z","updated_at":"2022-10-30T13:18:23.000Z","dependencies_parsed_at":"2025-01-20T09:50:32.776Z","dependency_job_id":null,"html_url":"https://github.com/MatteoFasulo/Paris-Euler","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/MatteoFasulo/Paris-Euler","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MatteoFasulo%2FParis-Euler","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MatteoFasulo%2FParis-Euler/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MatteoFasulo%2FParis-Euler/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MatteoFasulo%2FParis-Euler/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MatteoFasulo","download_url":"https://codeload.github.com/MatteoFasulo/Paris-Euler/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MatteoFasulo%2FParis-Euler/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31491094,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-06T17:22:55.647Z","status":"ssl_error","status_checked_at":"2026-04-06T17:22:54.741Z","response_time":112,"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":["data-science","jupyter-notebook","map","matplotlib","network-analysis","networkx","openstreetmap","pandas","python","social-network-analysis","transport"],"created_at":"2024-11-19T08:10:51.843Z","updated_at":"2026-04-06T21:30:57.468Z","avatar_url":"https://github.com/MatteoFasulo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Multiplex Network Analysis Public Transportation\r\n## University Project\r\nSocial Network Analysis Project\r\n\r\n### Paper\r\nThe paper associated to this project is available [here](https://github.com/MatteoFasulo/Paris-Euler/blob/main/Multiplex_Network_Analysis.pdf)\r\n\r\n### Multiplex Class\r\nThe `Multiplex` Class written in python is a nx.Graph wrapper for creating multi-layer networks and includes:\r\n* Adding layers to a multiplex\r\n* Linking the layers by proximity using [Haversine formula](https://en.wikipedia.org/wiki/Haversine_formula).\r\n* Writing networks back to NetworkX\r\n* Summarizing a multilayer network\r\n\r\n### Maps\r\n* [Stations and roads](https://matteofasulo.github.io/Paris-Euler/maps/france.html)\r\n* [Public transport lines](https://matteofasulo.github.io/Paris-Euler/maps/france_transport.html)\r\n\u003e **Tip:** Our map can be modified using the draw icons in top-left corner and then exported as GeoJSON file through the `export` button\r\n\r\n### Datasets\r\n- The GeoJSON file is available at [ComplexNetTSP GitHub](https://github.com/ComplexNetTSP/MultilayerParis) or in `geojson` folder.\r\n\u003e **Error:** The file cannot be viewed since has no escape character at the end of each line. Download it.\r\n\r\n\r\n### Libraries\r\n\r\n| Name | Description |\r\n| ------------- | ------------------------------ |\r\n| [Numpy] | package for scientific computing with Python.\r\n| [Pandas]| fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.\r\n| [Folium]| folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library.\r\n| [Os]| this module provides a portable way of using operating system dependent functionality.\r\n| [Json]| the json library can parse JSON from strings or files.\r\n| [Math]| access to the mathematical functions defined by the C standard.\r\n| [Random]| pseudo-random number generators for various distributions.\r\n| [NetworkX]| package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.\r\n| [PowerLaw]| package for Analysis of Heavy-Tailed Distributions\r\n| [EmpiricalDist]| library that represents empirical distribution functions.\r\n| [Matplotlib]| library for creating static, animated, and interactive visualizations in Python.\r\n| [Plotly]| graphing library makes interactive, publication-quality graphs.\r\n| [Kaleido]| cross-platform library for generating static images (e.g. png, svg, pdf, etc.) for web-based visualization libraries.\r\n| [MPL Toolkits]| matplotlib module for 3d plots.\r\n\r\n\r\n### Classes:                \r\n         \r\n| Class                   | Description                    |\r\n| -------------------------- | ------------------------------ |\r\n| `ParisTransportation`                       |GeoJSON preprocessing|\r\n| `MapMaker(ParisTransportation)`              |Nodes map creation|\r\n| `TransportMap`              |Public transport line creation + road network|\r\n| `Multiplex`             |Thin nx.Graph wrapper for multi-layer networks|\r\n| `LayeredNetworkGraph`             |3D plot using GraphViz of public transport networks|\r\n----\r\n\r\n[os]: \u003chttps://docs.python.org/3/library/os.html\u003e\r\n[json]: \u003chttps://docs.python.org/3/library/json.html\u003e\r\n[Numpy]: \u003chttps://numpy.org/install/\u003e\r\n[Pandas]: \u003chttps://pandas.pydata.org/\u003e\r\n[Folium]: \u003chttps://python-visualization.github.io/folium/\u003e\r\n[Math]: \u003chttps://docs.python.org/3/library/math.html\u003e\r\n[Random]: \u003chttps://docs.python.org/3/library/random.html\u003e\r\n[NetworkX]: \u003chttps://networkx.org/\u003e\r\n[PowerLaw]: \u003chttps://pypi.org/project/powerlaw/\u003e\r\n[EmpiricalDist]: \u003chttps://pypi.org/project/empiricaldist/\u003e\r\n[Matplotlib]: \u003chttps://matplotlib.org/\u003e\r\n[Plotly]: \u003chttps://plotly.com/python/\u003e\r\n[Kaleido]: \u003chttps://pypi.org/project/kaleido/\u003e\r\n[MPL Toolkits]: \u003chttps://matplotlib.org/stable/tutorials/toolkits/mplot3d.html\u003e\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatteofasulo%2Fparis-euler","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatteofasulo%2Fparis-euler","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatteofasulo%2Fparis-euler/lists"}