A NetworkX implementation of "Fast Local Community Discovery: Relying on the Strength of Links".
Community detection methods aim to find nodes connected to each other more than the other nodes in a graph. Despite the advantages of global methods, as they explore the entire network, they suffer from severe limitations due to large networks' sizes or a global criterion used indiscriminately throughout the whole network. Thus, some have focused on another fundamental problem, local community discovery, an egocentric function aiming to find the community that contains a query node. There are various local community search algorithms, including motif-based methods and modularity-based methods. Despite the advantages of the local modularity based practices, they recently obtained less attention due to two significant issues. These methods add many outliers to the community, especially when using on large dense networks. They are also sensitive to which query node of a particular community is used to start the expansion. We introduce a novel approach that can locally discover the communities precisely, deterministically, and quickly. It can also be used iteratively to detect the entire partitioning of a network with or without considering overlapping communities and concurrently identify outliers. This method works in a one-node-expansion model based on a notion of strong and weak links in a graph.
This repository provides Networkx implementation for LSWL and LSWL+ described in
Fast Local Community Discovery: Relying on the Strength of Links.
The original codes are lswl_offline.py, which finds the communities of nodes in a given network (the fast implementation), lswl_online.py, which does the same thing when the intended network is too large extensive memory consumption needs to be avoided (the memory efficient implementation). The last is lswl_plus.py, which uses our novel local discovery framework to detect the entire community structure of a given network by iteratively applying the LSWL approach to the unexplored parts of the network. The LSWL+ is capable of finding a partition with overlapping communities or without them, based on user preferences. This method can also find outliers (peripheral nodes of the graph that are marginally connected to communities) and hubs (nodes that bridge the communities). Modularity R and Modularity M are two methods initially presented in the following references, which we implemented for evaluation purposes.
@article{Clauset2005,
title = {Finding local community structure in networks},
volume = {72},
number = {2},
journal = {Physical Review E},
author = {Clauset, Aaron},
year = {2005},
pages = {026132},
}
@inproceedings{Luo2006,
year = {2006},
author = {Feng Luo and James Wang and Eric Promislow},
title = {Exploring Local Community Structures in Large Networks},
booktitle = {2006 {IEEE}/{WIC}/{ACM} International Conference on Web Intelligence ({WI}{\textquotesingle}06)},
pages = {233--239},
}
The codebase is implemented in Python 3.8.5 package versions used for development are just below.
networkx 2.5
argparse 1.1
This repository also contains the synthetic networks we generated via the LFR benchmark for our paper's evaluation section. Any code in this repository takes the edge list of a graph, in which any line indicates an edge between two nodes separated by \t. In addition to the network file, a file containing all query nodes should exist. In this file, each line has a node used as the start node to discover its community.
--strength_type '1': strengths between [-1,+1] and '2': strengths between [0,1]. Default is '2'.
--network The address of the network in form of edge list. No default value.
--timeout The maximum time in which LSWL should retrieve the community. Default is 1 second.
--output The address of the file to store the results. Default is './community.dat'.
for [lswl_offline.py] and [lswl_online.py]:
--query_nodes The address of the list of query nodes. No default value.
for [lswl_plus.py]:
--outlier If outliers need to merge into communities (y/n). Default is 'y'.
--overlap If overlapping communities need to be detected (y/n). Default is 'n'.
The following commands are examples demonstrating how each code in this repository can be executed:
$ python lswl_offline.py -n karate_edge_list.txt -q karate_query_nodes.txt -s 1
$ python lswl_online.py -n karate_edge_list.txt -q karate_query_nodes.txt -s 2
$ python lswl_plus.py -n karate_edge_list.txt -i y
$ python mod_r.py -n karate_edge_list.txt -q karate_query_nodes.txt
$ python mod_m.py -n karate_edge_list.txt -q karate_query_nodes.txt
Feel free to have a look at different parameters of each code via:
$ python [code_name.py] -h