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small-worlds/utils.py

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"""
NOTEs:
- This file is note meant to be run, it's just a collection of functions that are used in the other files. It's just a way to keep the code clean and organized.
- Why do I use os.path.join and not the "/"? Because it's more portable, it works on every OS, while "/" works only on Linux and Mac. In windows you would have to change all the "/" with "\". With os.path.join you don't have to worry about it and, as always, f*** Microsoft.
"""
from multiprocessing import Pool
import itertools
import os
import random
import wget
import zipfile
import pandas as pd
import tqdm as tqdm
import networkx as nx
from typing import Literal
from itertools import combinations
import plotly.graph_objects as go
from collections import Counter
import numpy as np
import gdown
from networkx.utils import py_random_state
import shutil
from pyvis.network import Network
# ------------------------------------------------------------------------#
def download_datasets():
"""
Download the datasets from the web and unzip them. The datasets are downloaded from the SNAP website and from a Google Drive folder.
Parameters
----------
None
Returns
-------
None
Notes
-----
The datasets are downloaded in the "data" folder. If the folder doesn't exist, it will be created. If the dataset is already downloaded, it will be skipped. The files are renamed to make them more readable.
"""
dict = {
"brightkite": ["https://snap.stanford.edu/data/loc-brightkite_edges.txt.gz", "https://snap.stanford.edu/data/loc-brightkite_totalCheckins.txt.gz"],
"gowalla": ["https://snap.stanford.edu/data/loc-gowalla_edges.txt.gz", "https://snap.stanford.edu/data/loc-gowalla_totalCheckins.txt.gz"],
"foursquare": ["https://drive.google.com/file/d/1PNk3zY8NjLcDiAbzjABzY5FiPAFHq6T8/view?usp=sharing"]
}
if not os.path.exists("data"):
os.mkdir("data")
print("Created data folder")
for folder in dict.keys():
if not os.path.exists(os.path.join("data", folder)):
os.mkdir(os.path.join("data", folder))
print("Created {} folder".format(folder))
## DOWNLOADING ##
for folder in dict.keys():
for url in dict[folder]:
if folder == "foursquare":
if not os.path.exists(os.path.join("data", folder, "foursquare_full.zip")):
output = os.path.join("data", folder, "foursquare_full.zip")
gdown.download(url, output, quiet=False, fuzzy=True)
else :
print("{} already downloaded".format(url))
else:
if not os.path.exists(os.path.join("data", folder, url.split("/")[-1])):
print("Downloading {}...".format(url))
wget.download(url, os.path.join("data", folder))
else :
print("{} already downloaded".format(url))
## UNZIPPING ##
for folder in dict.keys():
for file in os.listdir(os.path.join("data", folder)):
if file.endswith(".gz"):
print("Unzipping {}...".format(file))
os.system("gunzip {}".format(os.path.join("data", folder, file)))
elif file.endswith(".zip"):
print("Unzipping {}...".format(file))
os.system("unzip -o {} -d {}".format(os.path.join("data", folder, file), os.path.join("data", folder)))
os.remove(os.path.join("data", folder, file))
## FOURSQUARE CLEANING ##
for file in os.listdir(os.path.join("data", "foursquare", "dataset_WWW2019")):
if file.endswith(".txt"):
os.rename(os.path.join("data", "foursquare", "dataset_WWW2019", file), os.path.join("data", "foursquare", file))
for file in ["dataset_WWW_friendship_old.txt", "dataset_WWW_readme.txt", "raw_Checkins_anonymized.txt"]:
os.remove(os.path.join("data", "foursquare", file))
shutil.rmtree(os.path.join("data", "foursquare", "dataset_WWW2019"))
shutil.rmtree(os.path.join("data", "foursquare", "__MACOSX"))
os.rename(os.path.join("data", "foursquare", "dataset_WWW_friendship_new.txt"), os.path.join("data", "foursquare", "foursquare_friends_edges.txt"))
os.rename(os.path.join("data", "foursquare", "dataset_WWW_Checkins_anonymized.txt"), os.path.join("data", "foursquare", "foursquare_checkins_full.txt"))
## BRIGHTKITE CLEANING ##
os.rename(os.path.join("data", "brightkite", "loc-brightkite_totalCheckins.txt"), os.path.join("data", "brightkite", "brightkite_checkins_full.txt"))
os.rename(os.path.join("data", "brightkite", "loc-brightkite_edges.txt"), os.path.join("data", "brightkite", "brightkite_friends_edges.txt"))
## GOWALLA CLEANING ##
os.rename(os.path.join("data", "gowalla", "loc-gowalla_totalCheckins.txt"), os.path.join("data", "gowalla", "gowalla_checkins_full.txt"))
os.rename(os.path.join("data", "gowalla", "loc-gowalla_edges.txt"), os.path.join("data", "gowalla", "gowalla_friends_edges.txt"))
# ------------------------------------------------------------------------#
def create_graph_from_checkins(dataset: Literal['brightkite', 'gowalla', 'foursquareEU', 'foursquareIT'], create_file = True) -> nx.Graph:
"""
Create a graph from the checkins of the dataset. The graph is undirected and the nodes are the users and the edges are the checkins in common.
Parameters
----------
`dataset` : Literal['brightkite', 'gowalla', 'foursquare']
The dataset to use.
`create_file` : bool, optional
If True, the graph is saved in a file, by default True
Returns
-------
`G` : networkx.Graph
Raises
------
ValueError
If the dataset is not valid.
"""
if dataset not in ['brightkite', 'gowalla', 'foursquare']:
raise ValueError("Dataset not valid. Please choose between brightkite, gowalla, foursquare")
file = os.path.join("data", dataset, dataset + "_checkins.txt")
print("\nCreating the graph for the dataset {}...".format(dataset))
df = pd.read_csv(file, sep="\t", header=None, names=["user_id", "venue_id"], engine='pyarrow')
G = nx.Graph()
venues_users = df.groupby("venue_id")["user_id"].apply(set)
for users in tqdm.tqdm(venues_users):
for user1, user2 in combinations(users, 2):
G.add_edge(user1, user2)
# path to the file where we want to save the graph
edges_path = os.path.join("data", dataset , dataset + "_checkins_edges.tsv")
print("Done! The graph has {} edges".format(G.number_of_edges()), " and {} nodes".format(G.number_of_nodes()))
# delete from memory the dataframe
del df
if create_file:
# save the graph in a file
nx.write_edgelist(G, edges_path, data=True, delimiter="\t", encoding="utf-8")
return G
# ------------------------------------------------------------------------#
def create_friendships_graph(dataset: Literal['brightkite', 'gowalla', 'foursquareEU', 'foursquareIT']) -> nx.Graph:
"""
Create the graph of friendships for the dataset brightkite, gowalla or foursquare.
The graph is saved in a file.
Parameters
----------
`dataset` : str
The dataset for which we want to create the graph of friendships.
Returns
-------
`G` : networkx.Graph
The graph of friendships.
Notes
-----
Since we are taking sub-samples of each check-ins dataset, we are also taking sub-samples of the friendship graph. A user is included in the friendship graph if he has at least one check-in in the sub-sample.
"""
if dataset not in ["brightkite", "gowalla", "foursquare"]:
raise ValueError("The dataset must be brightkite, gowalla or foursquare")
file = os.path.join("data", dataset, dataset + "_friends_edges.txt")
df_friends_all = pd.read_csv(file, sep="\t", header=None, names=["node1", "node2"], engine='pyarrow')
unique_friends = set(df_friends_all["node1"].unique()).union(set(df_friends_all["node2"].unique()))
df_checkins = pd.read_csv(os.path.join("data", dataset, dataset + "_checkins_edges.tsv"), sep="\t", header=None, names=["node1", "node2"])
unique_checkins = set(df_checkins["node1"].unique()).union(set(df_checkins["node2"].unique()))
unique_users = unique_friends.intersection(unique_checkins)
df = df_friends_all[df_friends_all["node1"].isin(unique_users) & df_friends_all["node2"].isin(unique_users)]
df.to_csv(os.path.join("data", dataset, dataset + "_friends_edges_filtered.tsv"), sep="\t", header=False, index=False)
G = nx.from_pandas_edgelist(df, "node1", "node2", create_using=nx.Graph())
del df_friends_all, df_checkins, df
return G
# ------------------------------------------------------------------------#
def degree_distribution(G: nx.Graph, log: bool = True, save: bool = False) -> None:
"""
This function takes in input a networkx graph object and plots the degree distribution of the graph.
Parameters
----------
`G` : networkx graph object
The graph object
`log` : bool, optional
If True, the plot will be in log-log scale, by default True
`save` : bool, optional
If True, the plot will be saved in the folder "plots", by default False
Returns
-------
None
Notes
-----
Due to the characteristics of datasets, not using a log log scale will lead to a un-useful plot. Even if using a log scales alters the power-law distribution, it is still clearly visible and distinguishable from a poisson distribution (witch is what we are interested in in this case)
"""
degrees = [G.degree(n) for n in G.nodes()]
degreeCount = Counter(degrees)
fig = go.Figure()
fig.add_trace(go.Bar(x=list(degreeCount.keys()), y=list(degreeCount.values()), name='Degree Distribution'))
if log:
fig.update_layout(
title='Degree Distribution (log-log scale) of {}' .format(G.name),
xaxis_title='Degree',
yaxis_title='Number of Nodes',
xaxis_type='log',
yaxis_type='log',
width=800,
height=600,
template='plotly_white'
)
else:
fig.update_layout(
title='Degree Distribution of {}' .format(G.name),
xaxis_title='Degree',
yaxis_title='Number of Nodes',
width=800,
height=600,
template='plotly_white'
)
fig.show()
if save:
fig.write_image("plots/degree_distribution_{}.png".format(G.name))
# ------------------------------------------------------------------------#
def chunks(l, n):
"""
Auxiliary function to divide a list of nodes `l` in `n` chunks
Parameters
----------
`l` : list
List of nodes
`n` : int
Number of chunks
"""
l_c = iter(l)
while 1:
x = tuple(itertools.islice(l_c, n))
if not x:
return
yield x
# ------------------------------------------------------------------------#
def betweenness_centrality_parallel(G, processes=None, k =None) -> dict:
"""
Compute the betweenness centrality for nodes in a graph using multiprocessing.
Parameters
----------
G : graph
A networkx graph
processes : int, optional
The number of processes to use for computation.
If `None`, then it sets processes = 1
k : int, optional
Percent of nodes to sample. If `None`, then all nodes are used.
seed : int, optional
Seed for random number generator (default=None).
Returns
-------
dict
Notes
-----
Do not use more then 6 process for big graphs, otherwise the memory will be full. Do it only if you have more at least 32 GB of RAM. For small graphs, you can use more processes.
"""
# if process is None or 1, run the standard algorithm with one process
if processes is None or processes == 1:
print("\tRunning the networkx approximated algorithm with just one process")
G_copy = G.copy()
sample = int((k)*G_copy.number_of_nodes())
print("\tNumber of nodes after removing {} % of nodes: {}" .format((k)*100, G_copy.number_of_nodes()))
return np.mean(nx.betweenness_centrality(G, k=sample, seed=42).values())
if processes > os.cpu_count():
raise ValueError("The number of processes must be less than the number of cores in the system.")
if k is not None:
if (k < 0 or k > 1):
raise ValueError("k must be between 0 and 1.")
else:
G_copy = G.copy()
G_copy.remove_nodes_from(random.sample(G_copy.nodes(), int((k)*G_copy.number_of_nodes())))
print("\tNumber of nodes after removing {}% of nodes: {}" .format((k)*100, G_copy.number_of_nodes()))
print("\tNumber of edges after removing {}% of nodes: {}" .format((k)*100, G_copy.number_of_edges()))
if k is None:
G_copy = G.copy()
p = Pool(processes=processes)
node_divisor = len(p._pool) * 4
node_chunks = list(chunks(G_copy.nodes(), G_copy.order() // node_divisor))
num_chunks = len(node_chunks)
bt_sc = p.starmap(
nx.betweenness_centrality_subset,
zip(
[G_copy] * num_chunks, # this returns a list of Gs
node_chunks,
[list(G_copy)] * num_chunks, # this returns a list of lists of nodes
[True] * num_chunks,
[None] * num_chunks,
),
)
# Reduce the partial solutions
bt_c = bt_sc[0]
for bt in bt_sc[1:]:
for n in bt:
bt_c[n] += bt[n]
return bt_c
# ------------------------------------------------------------------------#
def average_shortest_path(G: nx.Graph, k=None) -> float:
"""
This function takes in input a networkx graph and returns the average shortest path length of the graph. This works also for disconnected graphs.
Parameters
----------
`G` : networkx graph
The graph to compute the average shortest path length of.
`k` : float
percentage of nodes to remove from the graph. If k is None, the average shortest path length of each connected component is computed using all the nodes of the connected component.
Returns
-------
float
The average shortest path length of the graph.
Raises
------
ValueError
If k is not between 0 and 1
"""
if k is not None and (k < 0 or k > 1):
raise ValueError("k must be between 0 and 1")
elif k is None:
G_copy = G.copy()
connected_components = list(nx.connected_components(G))
else:
G_copy = G.copy()
# remove the k% of nodes from G
G_copy.remove_nodes_from(random.sample(G_copy.nodes(), int((k)*G_copy.number_of_nodes())))
print("\tNumber of nodes after removing {}% of nodes: {}" .format((k)*100, G_copy.number_of_nodes()))
print("\tNumber of edges after removing {}% of nodes: {}" .format((k)*100, G_copy.number_of_edges()))
tmp = 0
connected_components = list(nx.connected_components(G_copy))
# remove all the connected components with less than 10 nodes
connected_components = [c for c in connected_components if len(c) > 10]
print("\tNumber of connected components with more then 10 nodes: {}" .format(len(connected_components)), "\r")
for C in (G_copy.subgraph(c).copy() for c in connected_components):
print("\tComputing average shortest path length of connected component with {} nodes and {} edges" .format(C.number_of_nodes(), C.number_of_edges()), "\r", end="")
tmp += nx.average_shortest_path_length(C)
return np.mean(tmp)
# ------------------------------------------------------------------------#
def average_clustering_coefficient(G: nx.Graph, k=None) -> float:
"""
This function takes in input a networkx graph and returns the average clustering coefficient of the graph. This works also for disconnected graphs.
Parameters
----------
G : networkx graph
The graph to compute the average clustering coefficient of.
k : int
percentage of nodes to remove from the graph. If k is None, the average clustering coefficient of each connected component is computed using all the nodes of the connected component.
Returns
-------
float
The average clustering coefficient of the graph.
Raises
------
ValueError
If k is not between 0 and 1
"""
if k is not None and (k < 0 or k > 1):
raise ValueError("k must be between 0 and 1")
elif k is None:
return nx.average_clustering(G)
else:
G_copy = G.copy()
G_copy.remove_nodes_from(random.sample(list(G_copy.nodes()), int((k)*G_copy.number_of_nodes())))
print("\tNumber of nodes after removing {}% of nodes: {}" .format((k)*100, G_copy.number_of_nodes()))
return nx.average_clustering(G_copy)
def generalized_average_clustering_coefficient(G: nx.Graph) -> float:
"""
Generalized definition of the average clustering coefficient of a graph. It better applies to small world networks and it's way more efficient than the average_clustering_coefficient function with the standard definition of the clustering coefficient.
Parameters
----------
G : networkx graph
The graph to compute the generalized average clustering coefficient of.
Returns
-------
float
The generalized average clustering coefficient of the graph.
"""
C = 0
for node in G.nodes():
k = G.degree(node)
C += (3*(k-1))/(2*(2*k - 1))
return C/G.number_of_nodes()
# ------------------------------------------------------------------------#
def create_random_graphs(G: nx.Graph, model = None, save = True) -> nx.Graph:
"""Create a random graphs of the same model of the original graph G.
Parameters
----------
G : nx.Graph
The original graph.
model : str
The model to use to generate the random graphs. It can be one of the following: "erdos", "watts_strogatz"
save: bool
If True, the random graph is saved in the folder data/random/model
Returns
-------
G_random : nx.Graph
"""
if model is None:
model = "erdos_renyi"
if model == "erdos_renyi":
G_random = nx.erdos_renyi_graph(G.number_of_nodes(), nx.density(G))
print("Creating a random graph with the Erdos-Renyi model {}" .format(G.name))
print("Number of edges in the original graph: {}" .format(G.number_of_edges()))
print("Number of edges in the random graph: {}" .format(G_random.number_of_edges()))
G_random.name = G.name + " Erdos-Renyi"
if save:
# check if the folder exists, otherwise create it
if not os.path.exists(os.path.join('data', 'random', 'erdos')):
os.makedirs(os.path.join('data', 'random', 'erdos'))
nx.write_gpickle(G_random, os.path.join('data', 'random', 'erdos', "erdos_" + str(G.number_of_nodes()) + "_" + str(G_random.number_of_edges()) + ".gpickle"))
print("\tThe file graph has been saved in the folder data/random/erdos with the syntax erdos_n_nodes_n_edges.gpickle")
return G_random
elif model == "watts_strogatz":
p = G.number_of_edges() / (G.number_of_nodes())
avg_degree = int(np.mean([d for n, d in G.degree()]))
G_random = nx.watts_strogatz_graph(G.number_of_nodes(), avg_degree, p)
print("Number of edges in the original graph: {}" .format(G.number_of_edges()))
print("Number of edges in the random graph: {}" .format(G_random.number_of_edges()))
G_random.name = G.name + " Watts-Strogatz"
if save:
# check if the folder exists, otherwise create it
if not os.path.exists(os.path.join('data', 'random', 'watts_strogatz')):
os.makedirs(os.path.join('data', 'random', 'watts_strogatz'))
nx.write_gpickle(G_random, os.path.join('data', 'random', 'watts_strogatz', "watts_strogatz_" + str(G.number_of_nodes()) + "_" + str(G_random.number_of_edges()) + ".gpickle"))
print("\tThe file graph has been saved in the folder data/random/watts_strogatz with the syntax watts_strogatz_n_nodes_n_edges.gpickle")
return G_random
def visualize_graphs(G: nx.Graph, k: float, connected = True):
"""
Function to visualize the graph in a HTML page using pyvis
Parameters
----------
G: nx.Graph
The graph to visualize
k: float
The percentage of nodes to remove from the graph. Default is None, in which case it will be chosen such that there are about 1000 nodes in the sampled graph. I strongly suggest to use the default value, other wise the visualization will be very slow.
connected: bool
If True, we will consider only the largest connected component of the graph
Returns
-------
html file
The html file containing the visualization of the graph
Notes:
------
This is of course an approximation, it's nice to have an idea of the graph, but it's not a good idea trying to understand the graph in details from this sampled visualization.
"""
if k is None:
if len(G.nodes) > 1500:
k = 1 - 1500/len(G.nodes)
else:
k = 0
# remove a percentage of the nodes
nodes_to_remove = np.random.choice(list(G.nodes), size=int(k*len(G.nodes)), replace=False)
G.remove_nodes_from(nodes_to_remove)
if connected:
# take only the largest connected component
connected_components = list(nx.connected_components(G))
largest_connected_component = max(connected_components, key=len)
G = G.subgraph(largest_connected_component)
# create a networkx graph
net = net = Network(directed=False, bgcolor='#1e1f29', font_color='white')
# for some reasons, if I put % values, the graph is not displayed correctly. So I use pixels, sorry non FHD users
net.width = '1920px'
net.height = '1080px'
# add nodes and edges
net.add_nodes(list(G.nodes))
net.add_edges(list(G.edges))
# set the physics layout of the network
net.set_options("""
var options = {
"edges": {
"color": {
"inherit": true
},
"smooth": false
},
"physics": {
"repulsion": {
"centralGravity": 0.25,
"nodeDistance": 500,
"damping": 0.67
},
"maxVelocity": 48,
"minVelocity": 0.39,
"solver": "repulsion"
}
}
""")
name = G.name.replace(" ", "_").lower()
if not os.path.exists("html_graphs"):
os.mkdir("html_graphs")
# save the graph in a html file
net.show("html_graphs/{}.html".format(name))
print("The graph has been saved in the folder html_graphs with the name {}.html" .format(name))
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