MaxPathHomology

Zhu, Z. and Chi, Z. (2024). "Recursive Computation of Path Homology for Stratified Digraphs".

This Python script is an implementation of the above paper that recursively computes the maximal (reduced) path homology of an unweighted stratified graph or unweighted directed acyclic graph (DAG).

Installation

To get started with this project, follow the steps below to install the necessary dependencies:

• Python: Ensure that Python 3.8 or higher is installed. You can check your Python version by running: bash python --version
• Clone the repository: bash git clone https://github.com/zhengtongzhu/DAG_MaxPathHomology.git cd DAG_MaxPathHomology
• Install dependencies: bash pip install -r requirements.txt

• Run the project: bash python recursive_algorithm.py

  You will see the following output from the test example: python lp: 2, sum_dim: 3, basis: [ [(-b0 + b1)*((-c0 + c1)*(-c3 + d1) + (c0 - c1)*(-c3 + d0)), (-a0 + a1)*(-b2 + b3)*(c2 - c3)], [(-b4 + b5)*(-c4 + c5)*(d2 - d3)] ]

dag_preprocess.py

The graph_preprocess.py module provides functions to compute l(G) and the stratified decomposition $G_*$ for a given unweighted DAG without multiple edges. These methods are described in Section 5.2 of the paper.

Example Usage

Consider a DAG G with the following edgelist:

python edgelist = [('a0', 'b2'), ('a0', 'b3'), ('a1', 'b2'), ('a1', 'b3'), ('b4', 'd1'), ('a1', 'c1'), ('b0', 'c0'), ('b0', 'c1'), ('b1', 'c0'), ('b1', 'c1'), ('b2', 'c2'), ('b2', 'c3'), ('b3', 'c2'), ('b3', 'c3'), ('b4', 'c4'), ('b4', 'c5'), ('b5', 'c4'), ('b5', 'c5'), ('b0', 'c2'), ('b0', 'c3'), ('b1', 'c2'), ('b1', 'c3'), ('b4', 'c2'), ('b4', 'c3'), ('b5', 'c2'), ('b5', 'c3'), ('c0', 'd0'), ('c0', 'd1'), ('c1', 'd0'), ('c1', 'd1'), ('c4', 'd2'), ('c4', 'd3'), ('c5', 'd2'), ('c5', 'd3'), ('a2', 'b4')]

each tuple (a, b) in this edgelist represents a directed unweighted edge in G from node a to node b. Below is a visualization of G:

The dag_process function first check if the DAG G contains multi-edges or has a loop (based on NetworkX):

python if len(edgelist) != len(set(edgelist)): raise ValueError("Error: The graph has duplicate edges.") G = nx.DiGraph(edgelist) if not nx.is_directed_acyclic_graph(G): raise ValueError("Error: The graph must be a DAG.")

then compute the longest path length of G (by daglongestpath_length):

python lp = nx.dag_longest_path_length(G)

We repeatedly prune the graph using prune and $G*$-algorithm (`lpedgelist) until the graph structure can no longer be simplified. The repetition of thepruneandlpedgelistprovides a set of weakly connected components{Gi}. These{Gi}are stratified graphs, and computing the direct sum of the maximal path homology of allGiis equivalent to computing the maximal path homology ofG, which are guaranteed by Corollary 3.5 and Proposition 3.7 of the paper. For theedgelistabove,10` edges are removed after pruning:

• ('a2', 'b4'): removed by prune.
• ('b0', 'c2'), ('b0', 'c3'), ('b1', 'c2'), ('b1', 'c3'), ('b4', 'c2'), ('b4', 'c3'), ('b5', 'c2'), ('b5', 'c3'), ('b4', 'd1'): removed by lp_edgelist.

The original graph G is splitted into two weakly connected components: ```python G_0 contains the following edges: [('a0', 'b2'), ('a0', 'b3'), ('a1', 'b2'), ('a1', 'b3'), ('a1', 'c1'), ('b2', 'c2'), ('b2', 'c3'), ('b3', 'c2'), ('b3', 'c3'), ('b0', 'c0'), ('b0', 'c1'), ('b1', 'c0'), ('b1', 'c1'), ('c0', 'd0'), ('c0', 'd1'), ('c1', 'd0'), ('c1', 'd1')]

G_1 contains the following edges: [('b4', 'c4'), ('b4', 'c5'), ('b5', 'c4'), ('b5', 'c5'), ('c4', 'd2'), ('c4', 'd3'), ('c5', 'd2'), ('c5', 'd3')] ``` The visual representation of these two components is shown below:

G_0

G_1

The dag_process returns 5 components of all G_i:

python subgraph_dict, node_counts, lp, num_graph, graph_list = dag_process(edgelist)

• The $i^{th}$ element of subgraph_dict is a dictionary represents G_i, where each key is a layer index (start from 0) and the value corresponding to the key is the set of nodes in that layer. For example, in the edgelist above, the subgraph_dict is:

python subgraph_dict = [ {0: {'b1', 'a1', 'b0', 'a0'}, 1: {'c1', 'b2', 'b3', 'c0'}, 2: {'c2', 'c3', 'd1', 'd0'}}, {0: {'b5', 'b4'}, 1: {'c5', 'c4'}, 2: {'d3', 'd2'}} ] c2 is a node in G_0's 2nd layer and c4 is a node in G_1's 1st layer.

• The i$^{th}$ element of node_counts represents the number of nodes in different layer of G_i. From the subgraph_dict above we know that

python node_counts = [ [4, 4, 4], [2, 2, 2] ] where [4, 4, 4] means G_0 has 4 nodes in layer 0, 4 nodes in layer 1, and 4 nodes in layer 2.

• lp is the longest path length of G as described above (lp = l(G)). Here lp = 2.
• num_graph is the cardinality of {G_i}. Here num_graph = 2.
• graph_list is a list where each element G_i is stored as an instance of the nx.DiGraph class.

recursive_algrithm.py

The recursive_algrithm.py includes the main algorithm max_path_homology to compute the maximal path homology for an unweighted stratified graph. Along with graph_preprocess.py, it also works for unweighted DAGs.

The max_path_homology returns lp, sum_dim and basis. python lp, sum_dim, basis = max_path_homology(edgelist, calculate_basis) Here: - lp = l(G) is the longest path length of G. - sum_dim is the sum of the Betti Numbers of the lp-dimentional (maximal) path homologies of all G_i. - If the input calculate_basis == True, then basis returns a basis of the lp-dimensional (maximal) reduced path homology of all G_i, otherwise it returns None.

If calculate_basis == True, the outputs are: python lp = 2 sum_dim = 3 basis = [ [(-b0 + b1)*((-c0 + c1)*(-c3 + d1) + (c0 - c1)*(-c3 + d0)), (-a0 + a1)*(-b2 + b3)*(c2 - c3)], [(-b4 + b5)*(-c4 + c5)*(d2 - d3)] ]

general_algorithm.py

The general_algorithm.py is based on an implementation from the following project:

Carranza, D., Doherty, B., Kapulkin, K., Opie, M., Sarazola, M., & Wong, L. Z. (2022). Python script for computing path homology of digraphs (Version 1.0.0) [Computer software]. https://github.com/sheaves/path_homology.

This script implements a general algorithm for computing reduced path homology. If you use this project or its comparative implementation in your work, please also cite the above project to acknowledge their contributions.

Other Modules

maxpph.py: Produces a decreasing persistence path homology plot (Section 6.2 of the paper).

experiment_func.py, stratified_gamma_1_2_3.py and stratified_gamma_4_5.py: Implement additional functions and simulations discussed in Section 6.1 of the paper.

maxph_matrix.py: Contains functions for matrix operations.

Citation

If you find this code useful, please cite it using the following BibTeX entry:

bibtex @software{Zhu_Computing_the_maximal_2024, author = {Zhu, Zhengtong and Chi, Zhiyi}, month = dec, title = {{Computing the maximal path homology of directed acyclic graph}}, url = {https://github.com/zhengtongzhu/DAG_MaxPathHomology}, version = {1.0.0}, year = {2024} }