openGraphMatching 0.1.7

A subgraph matching programming library.

openGraphMatching

A Python Graph/Subgraph Matching programming library. Based on openGraphMatching and the framework provided, you can develop and test different subgraph matching algorithms efficiently. What's more, since the codebase is Python-based, algorithms with neural networks can be integrated easily with traditional subgraph matching algorithms.

Read our report for more information.(Not provided now)

Usage

Prerequisite pytorch>=1.6, networkx, pytorch-geometric, deepsnap.

A detailed environment configration can be found in env.yml.

The NaiveMatch is the minium implementation of subgraph matching algorithm.

Here is the demo code for running GraphQL algorithm:

import openGraphMatching.matcher as matcher

# Prepare query graph q and target graph G in advance. q and G are networkx instance.

m = matcher.GQLMatcher(G) # Initialize the object with targer graph G
m.is_subgraph_match(q) # Run the check match process

Quickstart

• Clone this repository: git clone https://github.com/chang2000/openGraphMatching
• Install all prerequisite
  • networkx
  • pytorch-geometric
  • deepsnap
• pip install openGraphMatching to install this package.
• Go to examples and enjoy~

Misc

1. Validate the correctness of algorithm.

   • We use HPRD (9460 nodes, 34998 edges) to validate the correctness considering the time consuming and program performance.
   • We provides 200 quries(dense, 16 nodes).
   • A correctness checker is implemented in utils.py.
   • Expected result of all the matching are provided in expected.res. Refer to the dataset validate for more information.

2. The data format of the datasets

   1. For each .graph file, the first line will always be t x y where x and y are two int indicates the number of nodes and edges

   2. Vertex data v v_id v_label v_degree

3. Edge data e v_id v_id

Side word

• Cannot handle yeast well even it has a smaller dataset size, since around 10% of nodes left for 3000 nodes.