netcenlib 0.1.1

Network centrality library

NetCenLib

NetCenLib (Network centrality library) is a tool to compute a wide range of centrality measures for a given network. The library is designed to work with Python Networkx library.

Overview

The goal of NetCenLib is to offer a comprehensive repository for implementing a broad spectrum of centrality measures. Each year, new measures are introduced through scientific papers, often with only pseudo-code descriptions, making it difficult for researchers to evaluate and compare them with existing methods. While implementations of well-known centrality measures exist, recent innovations are frequently absent. NetCenLib strives to bridge this gap. It references the renowned CentiServer portal for well-known centrality measures and their originating papers, aiming to encompass all these measures in the future.

Code structure

All custom implementations are provided under netcenlib/algorithms package.

Implemented centrality measures:

• Algebraic
• Average Distance
• Barycenter
• Betweenness
• BottleNeck
• Centroid
• Closeness
• ClusterRank
• Communicability Betweenness
• Coreness
• Current Flow Betweenness
• Current Flow Closeness
• Decay
• Degree
• Diffusion degree
• Dispersion
• Eigenvector
• Entropy
• Geodestic k path
• Group Betweenness
• Group Closeness
• Group Degree
• Harmonic
• Heatmap
• Katz
• Laplacian
• Leverage
• Lin
• Load
• Mnc
• Pagerank
• Pdi
• Percolation
• Radiality
• Rumor
• Second Order
• Semi Local
• Subgraph
• Topological
• Trophic Levels
• Vote Rank

Code usage

Provided algorithms can be executed in the following ways:

• by importing and using specific method:

from typing import Any
import networkx as nx
from networkx import Graph
from netcenlib.algorithms.algebraic_centrality import algebraic_centrality

• using the CentralityService class, which provides access to all centralities through its properties.

from typing import Any
import networkx as nx
from networkx import Graph
from netcenlib.centrality import CentralityService

g: Graph = nx.karate_club_graph()
centrality_service = CentralityService(g)
centrality_centroid: dict[Any, float] = centrality_service.centroid

The biggest advantage of using CentralityService class is that it allows to compute all centralities at once. Users have all implementations within reach

• invoking compute_centrality method of CentralityService class, which allows to compute centrality for a given centrality measure.

import networkx as nx
from networkx import Graph

from netcenlib.centrality import compute_centrality
from netcenlib.taxonomies import Centrality

g: Graph = nx.karate_club_graph()
centrality_centroid: dict[Any, float] = compute_centrality(g, Centrality.CENTROID)

This method allows you not to directly specify centrality, making it easy to compute different centralities in a loop.

Contributing

For contributing, refer to its CONTRIBUTING.md file.

Maintainers

Project maintainers are:

• Damian Frąszczak
• Edyta Frąszczak