Social Networks Collection

What is this?

This repository serves as a collection of 38 (last updated this: 2020-09-09) social and information networks that I have investigated during my research, primarily for my M.S. thesis at the University of Vermont. Many of the networks are relatively small (0-1,000 nodes), but a few have as many as 100,000 nodes. These networks describe collaborations (musicians, scientists, etc.), friendships (human, animal, and fictional), blogging, emails, and more! Obviously there are many more networks that could be included; my criteria for selecting networks was a combination of: (i) relatively small size, (ii) personally interesting, (iii) popularity/citations, (iv) usefulness/importance. Code is written in Python and networkx is used to load the networks.

Disclaimer: I do not claim any of the data here as my own. This is merely a synthesis of others' works. In publication, please cite the authors of each network used. See the Sources section below for more information.

Why is this useful?

Many hours were spent finding, downloading, understanding, preprocessing, and loading these networks. In particular, downloading networks from disparate sources gives a range of file formats which have to be read in different ways. These lengthy steps present a significant barrier for researchers trying to quickly utilize several networks. Note: there are a couple similar projects which are worth checking into (i.e. Pajek).

I hope this can be a quick way to load and analyze some interesting social networks, particularly for people who are learning data science/social network analysis. You could study a single network or examine properties of the whole collection. Advanced researchers may use the whole collection for testing network algorithms or visualizations.

Important notes

1. During my research, I preprocessed the networks so that they were undirected, unweighted, static, and connected. Bipartite networks were projected onto one part. I also disregarded metadata if it was provided. In the future, I hope to make such options configurable.

2. The descriptions in network_descriptions.json are not always accurate because they correspond to the raw data (before preprocessing). Also, I copied the descriptions from the sources I got them -- so they may include typos or not be very informative. I hope to clean this up eventually. The citations and URLs provided should be accurate.

Usage

You can install the python package via pip install social-networks-collection 🛑 NOT READY YET! For now, please clone or download the repository and install the dependencies in requirements.txt. 🛑

1. To show network details and load into a networkx Graph

from read_networks import NETWORKS_DICT, get_network_info, read_any

#list all network names
print(list(NETWORKS_DICT.keys()))

#pick a network name and get more information
name = "Sampson's monastery"
print(get_network_info(name))

#load the network
G = read_any(name)

2. To visualize a network (see communities_visualization.py for a more complex example)

import networkx as nx
import matplotlib.pyplot as plt

nx.draw(G)
plt.show()

3. To compute network statistics (see compute_network_stats.py for details & further usage)

print("nodes:", nx.number_of_nodes(G))
print("edges:", nx.number_of_edges(G))
print("density:", density(G))

#alternatively, pick from a list of useful functions provided in STATS_DICT
from compute_network_stats import STATS_DICT 
print(list(STATS_DICT.keys()))

#these three lines are equivalent to the three earlier print statements
print("nodes:", STATS_DICT["num_nodes"](G))
print("edges:", STATS_DICT["num_edges"](G))
print("density:", STATS_DICT["density"](G))

#STATS_DICT gives easy access to more complex functions such as modularity
print("modularity:", STATS_DICT["modularity"](G))

Sources

Each network has a citation which can be accessed as follows

from read_networks import get_network_info
name = "Sampson's monastery"
print(get_network_info(name)["citation"])

Additionally, for each network I've included a URL where I retrieved the data. The URL either links to a website hosting the data or directly to the data file itself. The URL can similarly be accessed using get_network_info(name)["URL"].

Below is an extensive list of network databases

• Colorado Index of Complex Networks (ICON): https://icon.colorado.edu/#!/networks
• KONECT (the Koblenz Network Collection): http://konect.cc/networks/
• Stanford Network Analysis Project (SNAP) ~ Stanford Large Network Dataset Collection: https://snap.stanford.edu/data/index.html
• Pajek Datasets: http://vlado.fmf.uni-lj.si/pub/networks/data/
  • Exploratory Social Network Analysis with Pajek:
    http://vlado.fmf.uni-lj.si/pub/networks/data/esna/default.htm
  • UCINET IV Datasets: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm
• Personal web pages
  • Mark Newman: http://www-personal.umich.edu/~mejn/netdata/
  • Tore Opsahl: https://toreopsahl.com/datasets/
  • Uri Alon: https://www.weizmann.ac.il/mcb/UriAlon/download/collection-complex-networks
  • Alex Arenas: https://deim.urv.cat/~alexandre.arenas/data/welcome.htm
  • Albert-László Barabási: http://networksciencebook.com/translations/en/resources/data.html
• More UCI Datasets:
  • http://moreno.ss.uci.edu/data.html
  • https://sites.google.com/site/ucinetsoftware/datasets

Summary statistics for a few networks

Improvements

This repository is still in its infancy. Apart from including more networks, the code here can be improved a lot.

General

• Allow for configuring graph options: undirected/directed, unweighted/weighted, different versions of some networks.
• Convert all edgelist/adjacency matrix files to be in a common format would greatly shorten the code for all the read_*() functions. Removing unused raw data would save space on Github.
• Improve the info returned by get_network_info(), such as returning additional notes about preprocessing.

Software development best-practices & organization

• More extensive comments detailing function inputs & outputs. Improve documentation & test examples.
• Objective-oriented approach might be better (I started this in network.py...) -- especially if this grows into a bigger product.
• Lower strictness of requirements.txt; have yet to test with other versions.
• Reorganize utilities (edge_clustering_coeff.py, modularity.py, etc.) and global variables (NETWORKS_DICT, STATS_DICT, SMALL_NETWORKS).
• Reorganize repo structure (add more folders)
• Release as Python package