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https://github.com/BrennanAntone/awesome-teaching-about-networks

An awesome list of resources for teaching about networks and related topics
https://github.com/BrennanAntone/awesome-teaching-about-networks

List: awesome-teaching-about-networks

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An awesome list of resources for teaching about networks and related topics

Host: GitHub
URL: https://github.com/BrennanAntone/awesome-teaching-about-networks
Owner: BrennanAntone
License: cc0-1.0
Created: 2023-04-27T15:07:20.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2023-06-16T12:21:06.000Z (over 1 year ago)
Last Synced: 2024-04-10T08:01:25.326Z (5 months ago)
Size: 134 KB
Stars: 2
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- License: LICENSE
- Code of conduct: code-of-conduct.md

Awesome Lists containing this project

ultimate-awesome - awesome-teaching-about-networks - An awesome list of resources for teaching about networks and related topics. (Other Lists / PowerShell Lists)

README

        # Teaching About Networks [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

A compilation of resources for teaching about network science, social network analysis, and related topics:  

             ***teaching exercises***, ***research on teaching pedagogy***, ***software***, ***teaching materials***, and ***online communities***.

>

> This list was created to expand upon [2023](https://docs.google.com/document/d/1qp12aOCb77rLtb8pYORjAGx9pVWCLjJ3CHvRLubnjIs/edit?usp=sharing)/[2024](https://docs.google.com/document/d/1_44G5to8eQKu7ZE0nDcPPgzlfS9XMAQ7UW2wJZlBqhc/edit?usp=sharing) INSNA Sunbelt tutorials developed by Brennan Antone, Noshir Contractor, Kyosuke Tanaka, Y. Jasmine Wu, and Vsevolod Suschevskiy.

>

>The [Awesome project](https://github.com/sindresorhus/awesome) is a format of curated lists of

>resources for anyone interested in special topics. This format makes it easy for anyone to contribute

>to resources by making pull requests in Github (or email [email protected] with suggestions).

![](logo.png)

## Contents

- __[Interactive Teaching Exercises](#interactive-teaching-exercises)__

  - [Spotlighted teaching exercises](#spotlighted-teaching-exercises)

  - [Other teaching exercises](#other-teaching-exercises)

- __[Resources on Teaching Approaches/Pedagogy](#resources-on-teaching-approaches)__

- __[Networks Software](#networks-software)__

  - [R](#r)

  - [Python](#python)

  - [No-Code Software](#no-code-software)

- __[Teaching Materials](#teaching-materials)__

  - [Online Tutorial/Materials](#online-tutorial-materials)

  - [Textbooks](#textbooks)

  - [Videos and Lectures](#videos-and-lectures)

  - [Selected Readings](#selected-readings)

- __[Other Lists and Communities](#other-lists-and-communities)__

  - [Social Media and Listservs](#social-media-and-listservs)

  - [Relevant Awesome Lists](#relevant-awesome-lists)

- __[Contributing](#contributing)__

# Interactive Teaching Exercises

> Teaching about "network thinking" can be aided with sychronous or asynchronous teaching exercises that demonstrate applications of network analysis and prompt reflection around learners.

> Below is a list of short-term activities that can be used in courses, professional workshops, etc.

> Unless otherwise noted, all teaching exercises are available for free

## Spotlighted Teaching Exercises

## Other Teaching Exercises

-----

# Resources on Teaching Approaches

> Entries are ordered chronologically 

-[Teaching social network analysis](https://doi.org/10.1016/j.ijme.2023.100816) paper by Ion Georgiou (2023)

-----

# Networks Software

> There are three commonly used sets of software when teaching introductory network analysis - R packages, Python packages, and no-code solutions.

## R

- [Awesome R](https://github.com/qinwf/awesome-R) for general resources in R

## Python

- [Awesome Python](https://github.com/vinta/awesome-python) (other lists: [1](https://github.com/kirang89/pycrumbs), [2](https://github.com/svaksha/pythonidae), [3](https://github.com/trekhleb/learn-python)) for general resources in Python

- [Python for Computational Social Science and Digital Humanities](https://youtu.be/T7qMZH25co0), by Christopher Cameron, Cornell University (2023)

## No-Code Software

-----

# Teaching Materials

### Online Tutorial Materials

> See also the [Software](#software) section for material on software tools

- [CSC2552 Topics in Computational Social Science: AI, Data, and Society](https://www.cs.toronto.edu/~ashton/csc2552) - Seminar course taught by Ashton Anderson at the University of Toronto, Canada. 

- [NLP+CSS 201 Tutorials](https://nlp-css-201-tutorials.github.io/nlp-css-201-tutorials/) - Tutorials for advanced natural language processing methods designed for computational social science research.

- [SAGE collection of teaching material for Computational Social Science](https://ocean.sagepub.com/teaching-materials-for-computational-social-science) - Large collection of various teaching material for Computational Social Science

- [SICSS Learning Materials](https://sicss.io/overview) - Open source teaching and learning resources for computational social science

- [Social and Economic Networks: Models and Analysis](https://www.coursera.org/learn/social-economic-networks) - Online course on social and economic networks taught by Matthew O. Jackson

- [Toolkit for Digital Methods](https://wiki.helsinki.fi/display/TDM/Toolkit+for+Digital+Methods+Home) - A wiki of resources for digital methods in Social Sciences

- [UCCSS: University of California Computational Social Science](https://www.youtube.com/playlist?list=PLtjBSCvWCU3rJ_XAqaOr7NoQYWY005WCI) -  Multidisciplinary, multi-perspective, and multi-method guide on how to better understand society and human behavior with modern research tool.

- [APIs for Social Scientists](https://bookdown.org/paul/apis_for_social_scientists/)

- [Introduction to Computational Social Science in R](https://bookdown.org/markhoff/css/)

- [Introduction to Computational Social Science Methods with Python](https://github.com/gesiscss/css_methods_python)

- [Quanteda Tutorials for Quantitative Text Analysis in R](https://tutorials.quanteda.io)

- [R Course Material for Communication Science](https://github.com/ccs-amsterdam/r-course-material)

### Textbooks

> Entries are ordered chronologically

- [Networks, Crowds, and Markets: Reasoning About a Highly Connected World](https://www.cs.cornell.edu/home/kleinber/networks-book/), by David Easley and Jon Kleinberg (2010)

- [Computational Thinking for Social Scientists](https://jaeyk.github.io/comp_thinking_social_science/), by Jae Yeon Kim (2023)

### Videos and Lectures

- [A gentle introduction to network science](https://www.youtube.com/watch?v=L6CqqlILBCI), by Renaud Lambiotte, University of Oxford (2018)

- [Introduction to computational social science](https://youtu.be/EF7X9wwl0q4), by Matthew J. Salganik, Princeton University (2019)

### Selected Readings

> Important papers that provide a high-quality introduction to relevant topics. Ordered chronologically.

- [Computational Social Science and the Study of Political Communication](https://doi.org/10.1080/10584609.2020.1833121) by Yannis Theocharis and Andreas Jungherr (2020)

- [Computational Social Science and Sociology](https://doi.org/10.1146/annurev-soc-121919-054621) by Achim Edelmann, Tom Wolff, Danielle Montagne and Christopher A. Bail (2020)

- [Machine Learning for Social Science: An Agnostic Approach](https://doi.org/10.1146/annurev-polisci-053119-015921) by Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart (2021)

-----

# Other Lists and Communities

### Social Media and Listservs

- Facebook Group: [Teaching Social Networks](https://www.facebook.com/groups/293203241059192) 

- Facebook Group: [Big Data & Networks](https://www.facebook.com/groups/925927650775110)

- Email Listserv: [SOCNET](https://www.insna.org/socnet)

  

- Google Group: [Computational Social Science Network](https://groups.google.com/g/CSSNET)

- Subreddit: ["r/CompSocial"](https://www.reddit.com/r/CompSocial/)

- Subreddit: ["r/compsocialsci"](https://www.reddit.com/r/compsocialsci/)

- Subreddit: ["r/SocialNetworkAnalysis"](https://www.reddit.com/r/SocialNetworkAnalysis/)

- Subreddit: ["r/socialnetwork"](https://www.reddit.com/r/socialnetwork/)

- Subreddit: ["r/sna"](https://www.reddit.com/r/sna/)

- Subreddit: ["r/complexsystems/"](https://www.reddit.com/r/complexsystems/)

- Subreddit: ["r/Network_Analysis"](https://www.reddit.com/r/Network_Analysis/)

- Subreddit: ["r/networkscience"](https://www.reddit.com/r/networkscience/)

### Relevant Awesome Lists

Other well-curated [Awesome Lists]((https://github.com/sindresorhus/awesome) that may be relevant to network scientists.

- [Awesome Network Analysis](https://github.com/briatte/awesome-network-analysis)

|    |           |   |

|----------|:-------------:|------:|

| [Awesome Causality](https://github.com/napsternxg/awesome-causality) | [Awesome Digital Humanities](https://github.com/dh-tech/awesome-digital-humanities) | [Awesome Quarto](https://github.com/mcanouil/awesome-quarto)  |

| [Awesome Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection) | [Awesome Julia](https://github.com/greister/Awesome-Julia)  | [Awesome R](https://github.com/qinwf/awesome-R)  |

| [Awesome Computational Social Science](https://github.com/gesiscss/awesome-computational-social-science) | [Awesome Jupyter](https://github.com/markusschanta/awesome-jupyter) | [Awesome Research Software Registries](https://github.com/NLeSC/awesome-research-software-registries) |

| [Awesome Data Science with Python](https://github.com/r0f1/datascience) ([another](https://github.com/krzjoa/awesome-python-data-science)) | [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)  | [Awesome NLP](https://github.com/keon/awesome-nlp) ([another one](https://github.com/edobashira/speech-language-processing))  |

| [Awesome Data Science](https://github.com/academic/awesome-datascience) | [Awesome Notebooks](https://github.com/jupyter-naas/awesome-notebooks)  | [Awesome Scholarly Data Analysis](https://github.com/napsternxg/awesome-scholarly-data-analysis)  |

| [Awesome Data Visualization](https://github.com/javierluraschi/awesome-dataviz) | [Awesome Open Science](https://github.com/silky/awesome-open-science)  |  |

| [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)| [Awesome Python](https://github.com/vinta/awesome-python) (other lists: [1](https://github.com/kirang89/pycrumbs), [2](https://github.com/svaksha/pythonidae), [3](https://github.com/trekhleb/learn-python))  |  |

-----

# Contributing

Contributions welcome! Read the [contribution guidelines](contributing.md) first.

You can suggest changes via Github, or just email [email protected] if that is easier.