graph-measures 0.1.57

Description:

graphmeasures 0.1.57

Topological Graph Features
Topological feature calculators infrastructure.
Calculating Features
This package helps one to calculate features for a given graph. All features are implemented in python codes,
and some features have also an accelerated version written in C++. Among the accelerated features, one can find
a code for calculating 3- and 4-motifs using VDMC, a distributed algorithm to calculate 3- and 4-motifs in a
GPU-parallelized way.
Versions

Last version: 0.1.55 (most recommended)

What Features Can Be Calculated Here?
The set of all vertex features implemented in graph-measures is the following:



Feature
Feature's name in code
Is available in gpu?
Output size for directed graph
Output size for undirected graph




Average neighbor degree
average_neighbor_degree
NO
N x 1
N x 1


Degree^
degree
NO
N x 2
N x 1


In degree
in_degree
NO
N x 1
- - - - - - -


Out degree
out_degree
NO
N x 1
- - - - - - -


Louvain^^
louvain
NO
- - - - - - -
N x 1


Hierarchy energy
hierarchy_energy
NO




Motifs3
motif3
YES
N x 13
N x 2


Motifs4
motif4
YES
N x 199
N x 6


K core
k_core
YES
N x 1
N x 1


Attraction basin
attractor_basin
YES
N x 1
- - - - - - -


Page Rank
page_rank
YES
N x 1
N x 1


Fiedler vector
fiedler_vector
NO
- - - - - - -
N x 1


Closeness centrality
closeness_centrality
NO
N x 1
N x 1


Eccentricity
eccentricity
NO
N x 1
N x 1


Load centrality
load_centrality
NO
N x 1
N x 1


BFS moments
bfs_moments
NO
N x 2
N x 2


Flow
flow
YES
N x 1
- - - - - - -


Betweenness centrality
betweenness_centrality
NO
N x 1
N x 1


Communicability betweenness centrality
communicability_betweenness_centrality
NO
- - - - - - -
N x ?


Eigenvector centrality
eigenvector_centrality
NO
N x 1
N x 1


Clustering coefficient
clustering_coefficient
NO
N x 1
N x 1


Square clustering coefficient
square_clustering_coefficient
NO
N x 1
N x 1


Generalized degree
generalized_degree
NO
- - - - - - -
N x 16


All pairs shortest path length
all_pairs_shortest_path_length
NO
N x N
N x N



^ Degree - In the undirected case return the sum of the in degree and the out degree.
^^Louvain - Implement Louvain community detection method, then associate to each vertex the number of vertices in its community.
Aside from those, there are some other edge features.
Some more information regarding the features can be found in the files of features_meta.
Dependencies
setuptools
networkx==2.6.3
pandas
numpy
matplotlib
scipy
scikit-learn
python-louvain
bitstring
future
torch

How To Use The Accelerated Version (CPU/GPU)?
Both versions currently are not supported with the pip installation.
To use the accelerated version, one must use Linux operation system and Anaconda distribution, with the follow the next steps:


Go to the package's GitHub website and manually download:

The directory graphMeasures.
The python file runMakefileACC.py.

You might need to download a zip of the repository and extract the necessary files.


Place both the file and the directory inside your project, and run runMakefileACC.py.


Move to the boost environment: conda activate boost (The environment was created in step 2).


Use the package as explained in the section How To Use?


Installation Through pip
The full functionality of the package is currently available on a Linux machine, with a Conda environment.

Linux + Conda1. Go to base environment2. If pip is not installed on your env, install it. Then, use pip to install the package
Otherwise, pip must be installed.

pip install graph-measures

Note: On Linux+Conda the installation might take longer (about 5-10 minuets) due to the compilation of the c++ files.
How To Use?
Even though one has installed the package as graph-measures, The package should be imported from the code as graphMesaures. Hence, use:
from graphMeasures import FeatureCalculator

Calculating Features
There are two main methods to calculate features:

Using FeatureCalculator (recommended):
A class for calculating any requested features on a given graph.
The graph is input to this class as a text-like file of edges, with a comma delimiter, or a networkx Graph object.
For example, the graph example_graph.txt is the following file:
0,1
0,2
1,3
3,2

Now, an implementation of feature calculations on this graph looks like this:
import os
from graphMeasures import FeatureCalculator

# set of features to be calculated
feats = ["motif3", "louvain"]

# path to the graph's edgelist or nx.Graph object
graph = os.path.join("measure_tests", "example_graph.txt")

# The path in which one would like to save the pickled features calculated in the process.
dir_path = ""

# More options are shown here. For information about them, refer to the file.
ftr_calc = FeatureCalculator(path, feats, dir_path=dir_path, acc=True, directed=False,
gpu=True, device=0, verbose=True)

# Calculates the features. If one do not want the features to be saved,
# one should set the parameter 'should_dump' to False (set to True by default).
# If the features was already saved, you can set force_build to be True.
ftr_calc.calculate_features(force_build=True)
features = ftr_calc.get_features() # return pandas Dataframe with the features


Note: If one set acc=True without using a Linux+Conda machine, an exception will be thrown.
Note: If one set gpu=True without using a Linux+Conda machine that has cuda available on it, an exception will be thrown.



2. Using graphMeasure without FeatureCalculator (**less recommended**).

Edges motifs:
For now, you can calculate only motifs for edges. Unfortunately, you will have to do it separately from the nodes features.
There are two options for motif calculation - python version, and accelerated version (in CPP).
The python version is always available, but the accelerated version available only on linux machine
(the makefile targets linux, but the code should work for any os). Anyway, if you have a suitable machine,
the accelerated version is more recommended.
To run the accelerated version you should do:

Copy the graphMeasures directory to your project (available in this branch).
Open terminal in graphMeasures/edges_features/acc_features/acc/
Run the command make. If the makefile ends normally, a so file should be in a dir named bin.

Execution example:
import networkx as nx
from graphMeasures.edges_features.feature_calculator import FeatureCalculator

path = "./data/graph.txt"
gnx = nx.read_edgelist(path, delimiter=",", create_using=nx.DiGraph)
# acc signs if we will use the accelerated version.
calculator = FeatureCalculator(["motif3", "motif4"], gnx, acc=True)
calculator.build()

# The result will be a pandas Dataframe named calculator.df.
print(calculator.df)

Contact us
This package was written by Yolo lab's team from Bar-Ilan University.
For questions, comments or suggestions you can contact [email protected].