Awesome Network Analysis
A curated list of awesome resources for constructing, analyzing, and visualizing network data across various disciplines.
What is Awesome Network Analysis?
Awesome Network Analysis is a curated, community-maintained list of resources for network analysis and network science. It aggregates books, software, datasets, courses, and academic materials to help researchers and practitioners study relational data across disciplines like sociology, biology, economics, and history. The project organizes the scattered landscape of network analysis tools and literature into a single, accessible directory.
Researchers, data scientists, graduate students, and academics in fields such as sociology, political science, biology, computer science, and digital humanities who need to analyze or visualize network-structured data.
It saves significant time by providing a vetted, comprehensive collection of resources that would otherwise be dispersed across the web. As an open-source 'awesome list,' it benefits from community contributions to stay current with emerging tools and literature in the fast-evolving field of network science.
Overview
A curated list of awesome network analysis resources.
Use Cases
Best For
- Finding introductory textbooks and advanced monographs on network theory
- Discovering software libraries for network analysis in Python, R, or JavaScript
- Locating publicly available network datasets for research or benchmarking
- Identifying relevant academic journals and conferences in network science
- Learning network analysis through online courses and tutorials
- Exploring applications of network analysis in specific domains like history or ecology
Not Ideal For
- Teams needing the latest network analysis software releases or cutting-edge research papers, as the list is updated irregularly since 2016.
- Beginners seeking interactive, step-by-step tutorials with hands-on coding exercises, since it's a static resource aggregation without guided learning paths.
- Projects requiring proprietary or commercial network tools, as the focus is on open-source, academic, and publicly available resources.
- Developers looking for active community forums or real-time support channels, as it's a GitHub repository without built-in discussion features.
Pros & Cons
Pros
Curates hundreds of books, software tools across languages like Python and R, datasets, and academic materials into a single, well-organized list, saving researchers significant time in literature review and tool discovery.
Spans diverse fields from social networks and historical analysis to biological and economic systems, making it valuable for cross-disciplinary applications and specialized research needs.
Follows the 'awesome list' ethos with public contributing guidelines, allowing community contributions to keep the resource relevant and expanding, as noted in the README's philosophy.
Prioritizes publicly available datasets, open-source software, and free academic resources like full books online, lowering barriers to entry for students and underfunded researchers.
Cons
The README explicitly states it was started in 2016 and updated irregularly since, meaning some software versions, datasets, or research links may be outdated or missing recent advancements.
As a markdown file on GitHub, it lacks built-in search functionality, filtering options, or interactive elements, making navigation cumbersome for users with specific needs among hundreds of entries.
Without automated checks or active maintenance, dead links, deprecated tools, or superseded resources can accumulate over time, reducing reliability and requiring manual verification by users.
Frequently Asked Questions
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.