def generate_friends_network(user_count=10,
friends_count=get_friends_count(),
prob=get_prob(),
network_type='random',
conf_model=False):
# User list, probability p and network_type is given, construct a graph
res = "empty"
# generate specific graphs
if network_type.lower().find("small") >= 0:
# This is a small world network
print "Creating a small world network with " + str(
user_count) + " users"
rnd = snap.TRnd(1, 0)
new_graph = snap.GenSmallWorld(user_count, friends_count, prob, rnd)
elif network_type.lower().find("ring") >= 0:
# A ring graph
print "Creating a ring with " + str(user_count) + " users"
new_graph = snap.GenCircle(snap.PUNGraph, user_count, friends_count)
elif network_type.lower().find("power") >= 0:
# Power Law network
print "Creating a powerlaw network with " + str(user_count) + " users"
new_graph = snap.GenRndPowerLaw(user_count, friends_count, conf_model)
else:
# A random graph
print "Creating a random network with " + str(user_count) + " users"
edge_count = user_count * friends_count
new_graph = snap.GenRndGnm(snap.PUNGraph, user_count, edge_count)
save_graph_in_db(new_graph)
def gen_data():
graph = snap.GenRndGnm(snap.PNGraph, 300, 2400, True)
snap.SaveEdgeList(graph, "../data/Erdos-Renyi.txt")
graph = snap.GenPrefAttach(300, 8)
snap.SaveEdgeList(graph, "../data/PrefAttach.txt")
graph = snap.GenRndPowerLaw(300, 1.2)
snap.SaveEdgeList(graph, "../data/power-law.txt")
def scale_free_network(self):
d = SfDialog(self.root)
self.root.wait_window(d.top)
nodes = d.nodes
power = d.power
self.graph = snap.GenRndPowerLaw(nodes, power)
self.graph_name = "Scale-Free Network"
self.add_characteristics(self.graph, self.graph_name)
kmin = max(1, min(kmin, deg))
kmax = max(kmax, deg)
return 1. + np.log(graph.GetNodes()) / (np.log(kmax) - np.log(kmin))
def getBasicProps(graph):
# Assuming unweighted undirected graph
return {
'num_nodes': graph.GetNodes(),
'num_edges': graph.GetEdges(),
'avg_deg': 2. * graph.GetEdges() / graph.GetNodes(),
'gamma': getGamma(graph),
'deg_centr': getDegCentr(graph),
'num_tris': snap.GetTriads(graph, -1),
'global_cc': snap.GetClustCf(graph, -1),
}
Graph = snap.GenRndPowerLaw (1965206, 2.5)
prop = ['Scc','Wcc','InDeg','Clust']
for item in prop:
createFile(item,item,item)
OutDeg(Graph)
plotDegCorr(Graph)
print getBasicProps(Graph)
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
TSKS11 Hands-on session 4
Degree correlations
Erik G. Larsson, 2018
"""
import snap
import sys
sys.path.append("..")
sys.path.append("/courses/TSKS11/ht2019/data_and_fcns/")
from degreecorr import plot_knn
G = snap.GenRndPowerLaw(100000, 2.5)
snap.SaveEdgeList(G, "n1.edgelist.txt")
G = snap.GenRndPowerLaw(100000, 2.5)
snap.SaveEdgeList(G, "n2.edgelist.txt")
G = snap.GenRndPowerLaw(100000, 2.5)
snap.SaveEdgeList(G, "n3.edgelist.txt")
G = snap.GenRndPowerLaw(100000, 2.5)
snap.SaveEdgeList(G, "n4.edgelist.txt")
G = snap.GenRndPowerLaw(100000, 2.5)
snap.SaveEdgeList(G, "n5.edgelist.txt")
so_pr_ordered.append((
so_pr_iter.GetKey(),
so_pr_iter.GetDat(),
))
so_pr_iter.Next()
print("Top 3 nodes by PageRank (nodeId,PageRank):")
for kv_pair in so_pr_ordered[0:3]:
print(kv_pair)
#3
#Closeness, and degree centrality take single nodes as input
#Betweenness,Eigenvector and Page rank take a whole graph and return a hashtable
#top5% only
seed = snap.TRnd(1988)
exponent = 3
n = 400
g_rnd = snap.GenRndPowerLaw(n, exponent)
def all_node_centrality(g, centralityFunc):
centralities = snap.TIntFlt64H()
for node in g.Nodes():
centralities[node.GetId()] = centralityFunc(g, node.GetId())
return centralities
def iter_kv_pairs(hasht):
it = hasht.BegI()
while not it.IsEnd():
yield it.GetKey(), it.GetDat()
it.Next()
import snap # this fails G = snap.GenRndPowerLaw(1000, 1.374, True) print "True works!" # this works G = snap.GenRndPowerLaw(1000, 1.374, False) print "False works!"
import snap G = snap.GenRndPowerLaw(1000, 13.74, True)
def genScaleFree(N=5000, gamma=2.5):
return snap.GenRndPowerLaw(N, gamma)