Graph-Analysis-with-NetworkX

Graph Analysis with NetworkX

Notebook 1: Graph Types:

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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:

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

Notebook 3: Graph Statistics:

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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 with Center Visualized:

Karate Network with Periphery Visualized:

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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

Mutual Connection:

Min Node Cut Example:

Min Edge Cut Example:

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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:

Final Output of Averaging Centrality Ranking:

Notebook 4: Graph Link Prediction:

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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

Notebook 5: Graph Community Detection Algorithms:

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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:

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

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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: