{"id":18801252,"url":"https://github.com/velocitatem/modeling_search_engines","last_synced_at":"2026-03-07T07:34:15.730Z","repository":{"id":231893633,"uuid":"782974966","full_name":"velocitatem/modeling_search_engines","owner":"velocitatem","description":"This repository contains the code and resources for understanding and implementing search engine algorithms, with a specific focus on Google's PageRank.","archived":false,"fork":false,"pushed_at":"2024-05-16T12:01:06.000Z","size":2110,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-07T14:47:40.726Z","etag":null,"topics":["google","pagerank","search-engine"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/velocitatem.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-04-06T15:26:47.000Z","updated_at":"2024-05-16T12:01:10.000Z","dependencies_parsed_at":"2024-04-27T09:15:12.959Z","dependency_job_id":null,"html_url":"https://github.com/velocitatem/modeling_search_engines","commit_stats":null,"previous_names":["velocitatem/search_engine_research"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/velocitatem/modeling_search_engines","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/velocitatem%2Fmodeling_search_engines","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/velocitatem%2Fmodeling_search_engines/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/velocitatem%2Fmodeling_search_engines/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/velocitatem%2Fmodeling_search_engines/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/velocitatem","download_url":"https://codeload.github.com/velocitatem/modeling_search_engines/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/velocitatem%2Fmodeling_search_engines/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30209746,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-07T05:23:27.321Z","status":"ssl_error","status_checked_at":"2026-03-07T05:00:17.256Z","response_time":53,"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":["google","pagerank","search-engine"],"created_at":"2024-11-07T22:23:02.203Z","updated_at":"2026-03-07T07:34:10.718Z","avatar_url":"https://github.com/velocitatem.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Modeling Search Engines with Linear Algebra\n\n![Contributors](https://img.shields.io/badge/contributors-5-brightgreen.svg)\n[![Open in Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/velocitatem/modeling_search_engines/blob/main/engine.ipynb)\n\n\nWelcome to our project on **Modeling Search Engines Through Linear Algebra**! This repository contains the code and resources for understanding and implementing search engine algorithms, with a specific focus on Google's PageRank.\n\n## 📜 Abstract\nIn an increasingly interconnected world, understanding networks is key to designing and improving technology. This project explores the mathematics behind search engines and leverages linear algebra to model user behavior on the web. We focus on the PageRank algorithm, which evaluates the structure of web pages to determine their importance, offering a more reliable way of ranking web pages than traditional keyword-based methods.\n\n## 📁 Contents\n- **Code**: Jupyter notebook containing code for implementing and visualizing PageRank.\n\n## 🚀 Getting Started\n\n### Prerequisites\nEnsure you have the following installed:\n- Python 3.11\n- Jupyter Notebook\n- Required Python packages: `numpy`, `matplotlib`, `networkx`\n\n### Installation\nClone this repository:\n```bash\ngit clone https://github.com/velocitatem/modeling_search_engines\n```\nNavigate to the project directory:\n```bash\ncd Modeling-Search-Engines\n```\nInstall the required packages:\n```bash\npip install -r requirements.txt\n```\n\n### Running the Code\nTo explore the code and run the PageRank algorithm:\n1. Open the Jupyter notebook:\n   ```bash\n   jupyter notebook engine.ipynb\n   ```\n2. Follow the instructions within the notebook to execute the cells and visualize the results.\n\n## 📊 Features\n- **PageRank Implementation**: Understand and implement the PageRank algorithm using linear algebra.\n- **Network Graphs**: Visualize web structures as directed graphs.\n- **Interactive Visualization**: Explore an interactive network graph through a simple web interface.\n\n## 🎨 Visualization\nOur interactive network graph can be accessed [here](https://662a217970a974846a9569ac--magical-figolla-a3f256.netlify.app/). This graph showcases the interconnectedness of web pages and highlights the significance of each node based on PageRank.\n\n## 📖 Theory \u0026 Formulas\nThe project dives into the theory behind PageRank, including:\n- Adjacency matrices and transition matrices\n- Probability transition and eigenvectors\n- Handling sinks and infinite loops with damping factors\n\n## 🔍 Further Exploration\nThe project also compares PageRank with modern search algorithms such as semantic search and personalized search engines, highlighting the strengths and limitations of each.\n\n## 🌟 Contributors\n- Daniel Alves Rösel\n- Isabel de Valenzuela\n- Jaskaran Singh Ghai\n- Aswin Subramanian Maheswaran\n- Anna Payne\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvelocitatem%2Fmodeling_search_engines","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvelocitatem%2Fmodeling_search_engines","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvelocitatem%2Fmodeling_search_engines/lists"}