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Graph-Analysis-with-NetworkX

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:sparkler: Network/Graph Analysis with NetworkX in Python. Topics range from network types, statistics, link prediction measures, and community detection.

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# Graph-Analysis-with-NetworkX
Graph Analysis with NetworkX

# Dependencies:
The [`environment.yml`](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/environment.yml) YAML file in the root folder has the exact conda environment I used for this project.  
The [`requirements.txt`](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/requirements.txt) text file in the root folder has the exact Python environment I used for this project.  
* Option 1: **Run below with conda to create a new environment** to have the exact same environment I used for running the notebooks:  
`conda env create -f environment.yml` will create a conda environment called `network_analysis`.    
Then, you can run `conda env list` to view your existing environments.  
You can run `conda activate network_analysis` to use the new environment.

* Option 2: **If you don't want to use conda to create a environment**, you can try install Python packages I used with the following command:  
`pip install -r requirements.txt`

## Notebook 1: Graph Types:

[Click here to see the notebook](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/01_graph_types.ipynb)

This notebook covers how to create the following graphs using NetworkX:
1. Undirected graph
2. Directed graph
3. Signed graph
4. Weighted graph
5. Multigraph
6. Bipartite Graph
7. Projected Graph

## Notebook 2: Spring Layout:

[Click here to see the notebook](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/02_graph_layouts_spring_layout.ipynb)

This notebook covers how to create visualization using the spring layout in NetworkX for Genshin Impact character network:  
![genshin_impact_character_network](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/02_genshin_impact_character_network.png)

## Notebook 3: Graph Statistics:

[Click here to see the notebook part 1](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/03_graph_statistics_part_1.ipynb)

Notebook 3 part 1 covers how to calculate and interpret graph statistics for the following topic:
1. Triadic Closer:  
    * Local Clustering Coefficient (LCC)  
    * Global Clustering Coefficient (GCC): Average LCC and Transitivity  
2. Distance Measures:
    * Average Distance (Average Shortest Path Length)  
    * Eccentricity
    * Diameter
    * Radius
    * Center
    * Periphery

![Karate Network](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_karate_graph_overall.png)

#### Karate Network with Center Visualized:
![Karate Network with Center Visualized](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_karate_graph_center.png)

#### Karate Network with Periphery Visualized:
![Karate Network with Periphery Visualized](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_karate_graph_periphery.png)

[Click here to see the notebook part 2](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/03_graph_statistics_part_2.ipynb)

Notebook 3 part 2 covers how to calculate and interpret graph statistics for the following topic:
1. Connectivity:  
    * Strongly Connected
    * Weakly Connected
2. Robustness:
    * Density
    * Node Connectivity
    * Min Node CUt
    * Edge Connectivity
    * Min Edge Cut
    * Isolates

![Sucrose Neighbors](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_sucrose_neighbor_nodes.png)

#### Mutual Connection:
![Mutual Connection](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_genshin_impact_mutual_connection.png)

#### Min Node Cut Example:
![Min Node Cut](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_genshin_impact_min_node_cut.png)

#### Min Edge Cut Example:
![Min Edge Cut](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_genshin_impact_min_edge_cut.png)

[Click here to see the notebook part 3](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/03_graph_statistics_part_3.ipynb)

Notebook 3 part 3 covers how to calculate and interpret graph statistics for the following topic:
1. Centreality (Node Importance):  
    * Degree Centrality
    * CLoseness Centrality
    * Node Betweenness Centrality
    * Edge Betweenness Centrality
    * PageRank Centrality with PageRank Algorithm
    * Auth and Hub Centrality with HITS Algorithm
2. Centrality Ranking by Averging Centrality Measures
#### Summary Table for Centrality Measures:
![Summary Table for Centrality Measures](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_genshin_impact_centrality_summary.png)

#### Final Output of Averaging Centrality Ranking:
![Final Output of Averaging Centrality Ranking](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/03_genshin_impact_average_rank.png)

## Notebook 4: Graph Link Prediction:

[Click here to see the notebook part 1](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/04_link_prediction_measures_part_1.ipynb)

Notebook 4 part 1 covers how to calculate and interpret the below common link prediction features: 
1. Non-community Based Measures
    * The Number of Common Neighbors
    * Jaccard Coefficient
    * Resource Allocation Index
    * Adamic-Adar Index
    * Preferential Attachment
#### Visualizing the Common Neighbors for Potential Connection between Node 2 and Node 33
![Common Neighbors for Potential Connection between Node 2 and Node 33](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/04_karate_graph_common_neighbors_node_2_33.png)

## Notebook 5: Graph Community Detection Algorithms:

[Click here to see the notebook](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/05_community_detection.ipynb)

Notebook 5 covers how to use implemented community detection algorithms in NetworkX, python-louvain, and leidenalg
1. Community Detection Algorithms:
    * Girvan-Newman
    * Label-Propagation
    * Louvain
    * Leiden
#### Visualizing the community partition by Louvain Algorithm:
![Louvain Algorithm Partition Output](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/05_louvain_community_visualization.png)

## Notebook 6: Comparison of Louvain and Leiden for Community Detection:

[Click here to see the notebook](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/06_Louvain_Leiden_comparison.ipynb)

Stanford Network Analysis Project dataset is used for comparing performance: [DBLP collaboration network](https://snap.stanford.edu/data/com-DBLP.html)
| node total | edge total | Average clustering coefficient |
|------------|------------|--------------------------------|
| 317,080    | 1,049,866  | 0.6324                         |

Notebook 6 compares the community detection results using Louvain and Leiden algorithms in open source Python package called [python-louvain](https://github.com/taynaud/python-louvain) and [leidenalg](https://github.com/vtraag/leidenalg/). The notebook will highlight the disadvanatge sof Louvain algorithm and demonstrate why Leiden may be the algorithm you want to use for community detection.

|            | Louvain  | Leiden   |
|------------|----------|----------|
| Modularity | 0.821751 | 0.830028 |

|                        | Louvain | Leiden |
|------------------------|---------|--------|
| Disconnected Community | 5       | 0      |

#### Visualizing the disconnected community partition by Louvain Algorithm:
![Louvain Algorithm Partition Disconnected Output](https://github.com/KangboLu/Graph-Analysis-with-NetworkX/blob/main/graph_img/06_louvain_disconnected_community.png)

Graph-Analysis-with-NetworkX

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