Graph-Analysis-with-NetworkX

Graph Analysis with NetworkX

Dependencies:

The environment.yml YAML file in the root folder has the exact conda environment I used for this project. The 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

This notebook covers how to create the following graphs using NetworkX:

• Undirected graph
• Directed graph
• Signed graph
• Weighted graph
• Multigraph
• Bipartite Graph
• Projected Graph

Notebook 2: Spring Layout:

Click here to see the notebook

This notebook covers how to create visualization using the spring layout in NetworkX for Genshin Impact character network:

Notebook 3: Graph Statistics:

Click here to see the notebook part 1

Notebook 3 part 1 covers how to calculate and interpret graph statistics for the following topic:

• Triadic Closer:

* Local Clustering Coefficient (LCC) * Global Clustering Coefficient (GCC): Average LCC and Transitivity

• Distance Measures:

* Average Distance (Average Shortest Path Length) * Eccentricity * Diameter * Radius * Center * Periphery

Karate Network with Center Visualized:

Karate Network with Periphery Visualized:

Click here to see the notebook part 2

Notebook 3 part 2 covers how to calculate and interpret graph statistics for the following topic:

• Connectivity:

* Strongly Connected * Weakly Connected

• Robustness:

* Density * Node Connectivity * Min Node CUt * Edge Connectivity * Min Edge Cut * Isolates

Mutual Connection:

Min Node Cut Example:

Min Edge Cut Example:

Click here to see the notebook part 3

Notebook 3 part 3 covers how to calculate and interpret graph statistics for the following topic:

• 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

• Centrality Ranking by Averging Centrality Measures

Summary Table for Centrality Measures:

Final Output of Averaging Centrality Ranking:

Notebook 4: Graph Link Prediction:

Click here to see the notebook part 1

Notebook 4 part 1 covers how to calculate and interpret the below common link prediction features:

• 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

Notebook 5: Graph Community Detection Algorithms:

Click here to see the notebook

Notebook 5 covers how to use implemented community detection algorithms in NetworkX, python-louvain, and leidenalg

• Community Detection Algorithms:

* Girvan-Newman * Label-Propagation * Louvain * Leiden

Visualizing the community partition by Louvain Algorithm:

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

Click here to see the notebook

Stanford Network Analysis Project dataset is used for comparing performance: DBLP collaboration network | 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 and 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: