# Graph Theory

## Graph/GNN courses

1. [machine learning with graphs by Stanford](http://web.stanford.edu/class/cs224w/?fbclid=IwAR0nQR4lhyKCoTchsGQrcZ5E8EPBt2Bi4d8K8MYX-UN0ygQSxQ5bMoohhis), from ML to GNN.
2. [Graph deep learning course](https://geometricdeeplearning.com/lectures/) - graphs, sets,  groups,  GNNs. [youtube](https://www.youtube.com/watch?app=desktop\&v=w6Pw4MOzMuo)

## Graph Topics

1. [**General purpose and community detection GIT**](https://github.com/benedekrozemberczki/karateclub) **karate club bene**
2. **Connectivity**
3. **Min-cut:** [**1**](https://github.com/gsw73/min-cut/blob/master/karger_min_cut.py)**,** [**2**](https://github.com/ChuntaoLu/Algorithms-Design-and-Analysis/blob/master/week3%20Karger%20min%20cut/min_cut.py)**,** [**3**](https://github.com/WithaK16/kargerMinCut/blob/master/kargerMinCut.py)**, 4, 5, 6**
4. [**Louvain community**](https://github.com/taynaud/python-louvain/)
5. **Girwan newman** [**gist**](https://gist.github.com/chelsea1992/6c725a24d358763097bebe8223c2014a)**,** [**this worked**](https://github.com/ZwEin27/Community-Detection)**, t**[**his is potentially good too**](https://github.com/riteshkasat/Community-Detection-Algorithm)**,** [**another**](https://github.com/ServiceCutter/girvan-newman)**,** [**another**](https://github.com/ZwEin27/Community-Detection)**,** [**another**](https://github.com/kjahan/community)
6. [**Node2vec**](https://github.com/eliorc/Medium/blob/master/Nod2Vec-FIFA17-Example.ipynb)**,** [**paper**](https://arxiv.org/pdf/1607.00653.pdf)**,**  [**medium1**](https://towardsdatascience.com/think-your-data-different-ddc435f70850)**,** [**medium 2**](https://towardsdatascience.com/node2vec-embeddings-for-graph-data-32a866340fef) **- tutorial -** [**code**](https://github.com/eliorc/node2vec)**,** [**git code**](https://github.com/eliorc/Medium/blob/master/Nod2Vec-FIFA17-Example.ipynb)**,** [**original py2 code**](https://github.com/aditya-grover/node2vec)**,** [**taboola code for their medium paper**](https://github.com/taboola/node2vec-example/blob/master/node2vec.ipynb)
7. [**Evaluation metrics for community detection**](https://stackoverflow.com/questions/28952104/evaluation-metrics-for-community-detection-algorithms)
8. [**Review for community detection algorithms**](https://arxiv.org/pdf/0906.0612.pdf) **-** [**paper**](https://arxiv.org/abs/0906.0612)
9. [**Term: community structure**](https://en.wikipedia.org/wiki/Community_structure#Algorithms_for_finding_communities)
10. [**Term: modularity of networks**](https://en.wikipedia.org/wiki/Modularity_%28networks%29)
11. [**Unread paper**](http://science.sciencemag.org/content/328/5980/876)
12. [**Unread comparison of community detection algos**](https://arxiv.org/abs/1406.2205)
13. [**Clustering adjacency matrices**](https://stats.stackexchange.com/questions/125295/the-best-way-for-clustering-an-adjacency-matrix)
14. [**Spectral-clustering**](https://calculatedcontent.com/2012/10/09/spectral-clustering/) **(is this suppose to be here?)**
15. [**Finding natural groups in undirected graphs**](https://stats.stackexchange.com/questions/142297/finding-natural-groups-clusters-in-an-undirected-graph-over-several-undirect)
16. [**Awesome community detection on github**](https://github.com/benedekrozemberczki/awesome-community-detection?fbclid=IwAR3Ab2oh_skVqwUP6xOh-3G_t715eyPESzGhHQIVRogRFHK0SZ6dzoublqE)
17. [**Various algorithms**](https://neo4j.com/docs/graph-algorithms/current/algorithms/closeness-centrality/)

[**5. Centrality algorithms**](https://neo4j.com/docs/graph-algorithms/current/algorithms/centrality/)

1. [**5.1. The PageRank algorithm**](https://neo4j.com/docs/graph-algorithms/current/algorithms/page-rank/)
2. [**5.2. The Betweenness Centrality algorithm**](https://neo4j.com/docs/graph-algorithms/current/algorithms/betweenness-centrality/)
3. [**5.3. The Closeness Centrality algorithm**](https://neo4j.com/docs/graph-algorithms/current/algorithms/closeness-centrality/)
4. [**5.4. The Degree Centrality algorithm**](https://neo4j.com/docs/graph-algorithms/current/algorithms/degree-centrality/)

[**6. Community detection algorithms**](https://neo4j.com/docs/graph-algorithms/current/algorithms/community/)

1. [**6.1. The Louvain algorithm**](https://neo4j.com/docs/graph-algorithms/current/algorithms/louvain/)
2. [**6.2. The Label Propagation algorithm**](https://neo4j.com/docs/graph-algorithms/current/algorithms/label-propagation/)
3. [**6.3. The Connected Components algorithm**](https://neo4j.com/docs/graph-algorithms/current/algorithms/connected-components/)

[**7. Experimental algorithms**](https://neo4j.com/docs/graph-algorithms/current/experimental-algorithms/)

1. [**7.1. Procedures**](https://neo4j.com/docs/graph-algorithms/current/experimental-procedures/)
2. [**7.2. Centrality algorithms**](https://neo4j.com/docs/graph-algorithms/current/experimental-algorithms/centrality/)
3. [**7.3. Community detection algorithms**](https://neo4j.com/docs/graph-algorithms/current/experimental-algorithms/community/)
4. [**7.4. Path finding algorithms**](https://neo4j.com/docs/graph-algorithms/current/experimental-algorithms/pathfinding/)
5. [**7.5. Similarity algorithms**](https://neo4j.com/docs/graph-algorithms/current/experimental-algorithms/similarity/)
6. [**7.6. Link Prediction algorithms**](https://neo4j.com/docs/graph-algorithms/current/experimental-algorithms/linkprediction/)
7. [**7.7. Preprocessing functions and**](https://neo4j.com/docs/graph-algorithms/current/experimental-algorithms/preprocessing/)

## Graph Tools

1. [Graph-tool](https://graph-tool.skewed.de/) is an efficient Python module for manipulation and statistical analysis of graphs
2. [NetworkX](https://github.com/networkx/networkx) is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.<br>


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