Figure: Proposed EAGL Algorithm for summarizing a directed probabilistic graphical model based an available dataset.
A Jupyter notebook named "Example_GraphSummarization.ipynb" is provided for step by step guide to use the "EAGL" library for summarizing graphs. For the using the code directly from python IDE please use the file "Example_GraphSummarization.py" provided in the " Code " folder.
An implementation of Graph Laplacian for Directed Graphs can be found here. This was coded in Matlab. For a python implementation please check the NetworkX library.
- Enter if tree connection to be maintained (True or False): True
- Enter Tree Extraction method (dfs or bfs): bfs
- Enter Traversing Start Node(0 to 9): 0
- Enter Number of Iterations: 20
- Enter for which Eigenvalue (1st or 2nd) perform the calculation: 2
This will result in:
Number of Edges on the Updated DAG: 30
Compression: 40.0 %
Figure: Summarizing the Barabasi Albert Graph.
- Enter if tree connection to be maintained (True or False): True
- Enter Tree Extraction method (dfs or bfs): dfs
- Enter Traversing Start Node(0 to 9):0
- Enter Number of Iterations: 10
- Enter for which Eigenvalue (1st or 2nd) perform the calculation: 2
- Enter Cut-off Value: 0.05
Number of Edges on the Updated DAG: 38
Compression: 24.0 %
Figure: Summarizing the Barabasi Albert Graph.
- Scipy
- Numpy
- Networkx
- Matplotlib
*The code was tested using Python 3.6