9. Random walks#
import networkx as nx
import random
import matplotlib.pyplot as plt
import operator
import numpy as np
from tqdm import tqdm
9.1. Pagerank#
# select random graph using gnp_random_graph() function of networkx
Graph = nx.gnp_random_graph(10, 0.5, directed=True)
nx.draw(Graph, with_labels=True, node_color="green") # draw the network graph
plt.figure(figsize=(15, 10))
plt.show() # to show the graph by plotting it
<Figure size 1500x1000 with 0 Axes>
print(Graph)
DiGraph with 10 nodes and 43 edges
G = Graph
list(G.nodes)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
random_node = random.choice([i for i in range(Graph.number_of_nodes())])
random_node
6
dict_counter = {} # initialise the value for all nodes as 0
for i in range(Graph.number_of_nodes()):
dict_counter[i] = 0
# increment by traversing through all neighbors nodes
dict_counter[random_node] = dict_counter[random_node] + 1
dict_counter
{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 1, 7: 0, 8: 0, 9: 0}
# random_node is the start node selected randomly
random_node = random.choice([i for i in range(Graph.number_of_nodes())])
dict_counter = {} # initialise the value for all nodes as 0
for i in range(Graph.number_of_nodes()):
dict_counter[i] = 0
# increment by traversing through all neighbors nodes
dict_counter[random_node] = dict_counter[random_node] + 1
# Traversing through the neighbors of start node
for i in range(10000):
list_for_nodes = list(Graph.neighbors(random_node))
if len(list_for_nodes) == 0: # if random_node having no outgoing edges
random_node = random.choice([i for i in range(Graph.number_of_nodes())])
dict_counter[random_node] = dict_counter[random_node] + 1
else:
random_node = random.choice(
list_for_nodes
) # choose a node randomly from neighbors
dict_counter[random_node] = dict_counter[random_node] + 1
dict_counter
{0: 919,
1: 955,
2: 928,
3: 1107,
4: 1166,
5: 592,
6: 1180,
7: 1318,
8: 1400,
9: 436}
G.in_degree()
InDegreeView({0: 4, 1: 3, 2: 5, 3: 4, 4: 5, 5: 3, 6: 6, 7: 5, 8: 6, 9: 2})
G.out_degree()
OutDegreeView({0: 2, 1: 3, 2: 5, 3: 5, 4: 4, 5: 5, 6: 4, 7: 5, 8: 5, 9: 5})
# using pagerank() method to provide ranks for the nodes
rank_node = nx.pagerank(Graph)
# sorting the values of rank and random walk of respective nodes
sorted_rank = sorted(rank_node.items(), key=operator.itemgetter(1))
sorted_random_walk = sorted(dict_counter.items(), key=operator.itemgetter(1))
sorted_rank
[(9, 0.05375876382444118),
(5, 0.06448529823904973),
(0, 0.09467827660840808),
(1, 0.09558425994303228),
(2, 0.0987367296113627),
(3, 0.10510924897255257),
(4, 0.11196672087390377),
(6, 0.11420623966768598),
(7, 0.12925606977626905),
(8, 0.13221839248329473)]
sorted_random_walk
[(9, 436),
(5, 592),
(0, 919),
(2, 928),
(1, 955),
(3, 1107),
(4, 1166),
(6, 1180),
(7, 1318),
(8, 1400)]
[val for (node, val) in sorted_rank]
[0.05375876382444118,
0.06448529823904973,
0.09467827660840808,
0.09558425994303228,
0.0987367296113627,
0.10510924897255257,
0.11196672087390377,
0.11420623966768598,
0.12925606977626905,
0.13221839248329473]
fig, ax = plt.subplots()
ax.plot([val / 0.132 for (node, val) in sorted_rank])
ax.plot([val / 1400 for (node, val) in sorted_random_walk])
[<matplotlib.lines.Line2D at 0x1227a5970>]
from scipy.stats import spearmanr
spearmanr([x[0] for x in sorted_rank], [x[0] for x in sorted_random_walk])
SignificanceResult(statistic=0.9878787878787878, pvalue=9.307459988955517e-08)
9.1.1. prediction of pagerank#
def predicted_rank(in_degrees, q=0.85):
av = np.mean(in_degrees)
N = len(in_degrees)
return q / N + ((1 - q) / N) * (np.array(in_degrees) / av)
9.1.2. ER graph#
G = nx.DiGraph(nx.erdos_renyi_graph(200, 0.1))
q = 0.85
pr = nx.pagerank(G, alpha=1 - q)
in_degrees = [G.in_degree(x) for x in pr]
out_degrees = [G.out_degree(x) for x in pr]
fig = plt.figure(figsize=(12, 8))
plt.scatter(in_degrees, pr.values(), c=out_degrees, marker=".")
plt.colorbar(label="out degree")
plt.xscale("log")
plt.yscale("log")
plt.plot(sorted(in_degrees), predicted_rank(sorted(in_degrees), q), "k-")
plt.xlabel("in degree")
plt.ylabel("PageRank")
Text(0, 0.5, 'PageRank')
9.1.3. real world graph#
# filepath = "./../datasets/USairport_2010.txt"
filepath = "./../datasets/cit-HepTh.txt"
G = nx.DiGraph()
fh = open(filepath, "r")
for line in fh.readlines():
s = line.strip().split()
if s[0] != "#":
origin = int(s[0])
dest = int(s[1])
G.add_edge(origin, dest)
fh.close()
GCC = G.subgraph(list(nx.weakly_connected_components(G))[0])
GCC.number_of_nodes()
27400
ranks = nx.pagerank(GCC, alpha=1 - q)
ranks
{1001: 3.2602499246893044e-05,
9304045: 4.071306164225111e-05,
9308122: 6.479306620003078e-05,
9309097: 8.698076219806325e-05,
9311042: 4.5027491416976204e-05,
9401139: 0.0002062551248078757,
9404151: 5.263194784399839e-05,
9407087: 0.000657918655059675,
9408099: 0.000435401415075891,
9501030: 7.183653532234046e-05,
9503124: 0.0003809559588067761,
9504090: 0.00013857143647131768,
9504145: 8.315079544067203e-05,
9505025: 4.028424998714547e-05,
9505054: 4.735170114694794e-05,
9505105: 0.0001002915296066482,
9505162: 7.5393056368472e-05,
9506048: 4.515780210886138e-05,
9506112: 6.813057106676225e-05,
9506144: 5.227848260853375e-05,
9507050: 7.362582036791592e-05,
9507158: 0.00012099234013801616,
9508094: 4.9926041251757153e-05,
9508155: 6.809694853171543e-05,
9510142: 4.017626740799703e-05,
9510225: 7.76787039787459e-05,
9510234: 4.6642662505665174e-05,
9511030: 0.0001161820133031774,
9511171: 4.007831301786174e-05,
9601108: 4.9613164206003e-05,
9602022: 0.00016308837295732917,
9602114: 8.496961752560241e-05,
9603003: 7.573627848221192e-05,
9603150: 6.850686621787474e-05,
9603161: 8.997520303219774e-05,
9603167: 0.00013817270934315132,
9605184: 6.430931171528395e-05,
9605222: 3.722800499715411e-05,
9606017: 6.037863337033408e-05,
9606040: 6.161273749216551e-05,
9607163: 8.646986953640306e-05,
9607207: 6.402636901070692e-05,
9608086: 3.769839556950575e-05,
9609070: 4.7384518506636504e-05,
9609071: 4.474463479306141e-05,
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9611137: 5.8004717963157726e-05,
9612108: 4.0014512425059836e-05,
9701162: 6.0745752676542493e-05,
9702094: 5.054980277707597e-05,
9702155: 5.4570968627785315e-05,
9702198: 4.298288347070795e-05,
9703082: 4.507302473829063e-05,
9703166: 0.0001180071409930563,
9704097: 3.766475523693807e-05,
9705030: 4.326737606459631e-05,
9705044: 4.265027741926577e-05,
9705104: 4.5811819449376484e-05,
9705220: 3.743412495391929e-05,
9706005: 3.350317267634104e-05,
9707014: 3.438676640625028e-05,
9707042: 3.97050060074358e-05,
9707049: 3.717432303455635e-05,
9710230: 6.11455168493815e-05,
9711036: 3.4240150010454285e-05,
9711104: 3.507304916375624e-05,
9712028: 7.002770453495068e-05,
9712042: 3.42603591530865e-05,
9802194: 3.6473807280330665e-05,
9805056: 3.447480017288377e-05,
9805206: 3.3721846131536755e-05,
9806094: 3.5689881864827016e-05,
9810188: 0.00011350943395669165,
9811217: 3.5595577106267174e-05,
9905036: 3.501804099747471e-05,
9907041: 3.357834509793832e-05,
9908007: 3.496571360568869e-05,
9908144: 4.2849426977707676e-05,
9909108: 3.98878454041099e-05,
9909120: 3.969259124373852e-05,
9909229: 3.488755119473096e-05,
9910238: 3.341063239536191e-05,
9910248: 3.255387766035767e-05,
9910268: 3.2447682873858855e-05,
9204040: 4.616999515955385e-05,
9203084: 5.674302314580726e-05,
9204035: 6.413167025992615e-05,
9205041: 5.464839540107384e-05,
9207049: 3.9207326426503814e-05,
9207111: 4.105598708358358e-05,
9301042: 0.00018691239155548472,
9301043: 8.233060567247599e-05,
9201015: 6.45765997733874e-05,
9201040: 6.96376026940127e-05,
9201047: 3.859713775744476e-05,
9202046: 0.00011812538871736851,
9202059: 5.830778501617569e-05,
9202092: 8.476606268822707e-05,
9203008: 3.8938665711620004e-05,
9203031: 3.8251914719732607e-05,
9204037: 3.4574313932970444e-05,
9204046: 8.011395126897522e-05,
9205046: 6.711913242114635e-05,
9205060: 3.2968083378045116e-05,
9205062: 4.388600239953454e-05,
9205068: 0.0002507633624459461,
9206023: 3.6417122966609985e-05,
9206051: 4.8046504583369574e-05,
9206078: 7.110482172652614e-05,
9207016: 0.00012733108701231164,
9208076: 3.9816979566402154e-05,
9209052: 5.627184048923563e-05,
9210015: 4.667930782247373e-05,
9210021: 0.0001002957284581732,
9210121: 5.334568225855176e-05,
9211021: 0.00013474099032175584,
9211041: 3.666221242196717e-05,
9212089: 3.334958173888217e-05,
9301005: 3.626913094057395e-05,
9302033: 7.063954526018547e-05,
9302069: 3.564284510385992e-05,
9302126: 9.035188549664208e-05,
9303032: 3.676101186913829e-05,
9304155: 3.8273023989446705e-05,
9305185: 9.973722914452983e-05,
9308042: 3.7207101034227e-05,
9305139: 6.060629188598678e-05,
9309140: 9.176829413545297e-05,
9204102: 9.669701303514938e-05,
9211097: 8.430411540368639e-05,
9402002: 0.00017200566001889547,
9402005: 7.305783317894759e-05,
9402044: 0.00019428050617852094,
9403195: 9.28169395154302e-05,
9403198: 0.00010457218863547841,
9406128: 5.389817140563775e-05,
9205081: 8.07784156644802e-05,
9207053: 8.089983685768748e-05,
9208055: 6.737181273718996e-05,
9209016: 6.273299096246157e-05,
9211056: 6.606099624912443e-05,
9303057: 5.602812272209033e-05,
9305142: 5.967067861374172e-05,
9306052: 6.972567292170496e-05,
9306125: 3.419494350987383e-05,
9311120: 5.3524330602837517e-05,
9402032: 0.00014367559900607048,
9406105: 5.763008520903384e-05,
9406198: 4.15606576693375e-05,
9407021: 3.487684518751575e-05,
9407111: 5.365725553045676e-05,
9408040: 4.112054897874756e-05,
9408074: 0.00014518309971740535,
9408083: 6.534197679988518e-05,
9409021: 5.889140238162978e-05,
9410167: 0.00025317058292143306,
9410210: 3.245798763877829e-05,
9412184: 0.00019003164586420517,
9205027: 0.00014480156534656753,
9406012: 4.197775252178358e-05,
9411149: 0.00020174969209629361,
9411178: 5.086756967554843e-05,
9201059: 9.040293775485595e-05,
9309152: 3.707543084820519e-05,
9310067: 3.604826806796131e-05,
9412115: 3.343593156376933e-05,
9501068: 0.00014496578667303443,
9502072: 8.103416776557397e-05,
9504027: 7.119565625198763e-05,
9504047: 8.486913266371059e-05,
9304154: 0.0001420242598774016,
9501022: 4.4585122326363266e-05,
9502057: 4.188573475306877e-05,
9503179: 7.573332840982755e-05,
9308083: 6.809801018194223e-05,
9411048: 0.00011802739520778531,
9411057: 0.00010553183831590778,
9501111: 3.7824449431721964e-05,
9502077: 4.8106179471715196e-05,
9503163: 7.113469750781244e-05,
9504006: 7.456933140358978e-05,
9504034: 5.542076148443348e-05,
9505023: 4.29577643978692e-05,
9505053: 8.17736531662255e-05,
9505062: 7.829395082130192e-05,
9302103: 8.665321944070493e-05,
9305069: 4.3337361569125384e-05,
9406032: 3.699858786572521e-05,
9409188: 5.2329109639947464e-05,
9406055: 5.197512851049832e-05,
9408036: 5.575490935214403e-05,
9411234: 4.3178789105974464e-05,
9412200: 3.797608910294868e-05,
9506075: 3.936870163165323e-05,
9506091: 3.776083598708869e-05,
9506024: 3.4747649467102654e-05,
9506077: 5.455347274063504e-05,
9506110: 4.942441836485989e-05,
9506160: 6.015110047551846e-05,
9506194: 6.464593301975209e-05,
9507027: 4.22819938806695e-05,
9207060: 6.333589768465925e-05,
9503212: 4.708814380901866e-05,
9506126: 8.657530753097348e-05,
9506150: 4.8711002349850154e-05,
9507048: 7.91472719673411e-05,
9303106: 3.2558405952614415e-05,
9410073: 8.533291188920399e-05,
9411187: 6.469218293442048e-05,
9504081: 9.052319103593933e-05,
9504095: 4.4982591880122344e-05,
9506057: 4.389990863450227e-05,
9507090: 6.492181173425543e-05,
9508056: 3.2714138987655083e-05,
9304135: 4.443142771061796e-05,
9412209: 4.2029196506763824e-05,
9505073: 6.004468655441689e-05,
9505150: 7.077485139873566e-05,
9506084: 3.814210179438537e-05,
9506102: 7.216118054623693e-05,
9507113: 3.899099679592573e-05,
9507115: 5.139424511800246e-05,
9507121: 0.00011092015606332382,
9507151: 3.817772416668861e-05,
9507153: 3.609686318012628e-05,
9507168: 3.845999968375341e-05,
9508044: 3.273503322485601e-05,
9508064: 4.351082695777849e-05,
9508071: 3.735398752186731e-05,
9508096: 3.1925306612137044e-05,
9508143: 0.00013031102953390815,
9508144: 3.743792849355875e-05,
9409110: 3.857866372672934e-05,
9503208: 3.4611592670689406e-05,
9507012: 6.353923351230212e-05,
9508154: 6.178898500593902e-05,
9509009: 3.752273335920484e-05,
9510093: 4.226083245458504e-05,
9201056: 0.0001760100571783792,
9206080: 3.4789630281134144e-05,
9301083: 5.166872307301751e-05,
9307158: 5.3824855124609365e-05,
9308062: 3.324435429145554e-05,
9407031: 8.571675395095105e-05,
9506122: 4.4345398136395864e-05,
9509066: 0.00013260619260263158,
9510135: 0.00026461811238430915,
9510169: 0.0001342240511648053,
9204030: 6.583209531065423e-05,
9205009: 5.2803436604415e-05,
9510017: 0.0004029149662752657,
9510173: 4.406411712289736e-05,
9510182: 8.035517353481484e-05,
9412236: 5.2812932122909656e-05,
9511164: 6.073801745523166e-05,
9511222: 0.00010678699943244122,
9512016: 3.5430444734134954e-05,
9512017: 3.397524577908607e-05,
9512059: 0.00011636658513654782,
9512145: 3.911391336820646e-05,
9601014: 4.0412689948737606e-05,
9601038: 0.0001075598963964827,
9206101: 3.9631047604624616e-05,
9409111: 6.641633526784061e-05,
9505183: 4.493511848866699e-05,
9506101: 5.402489217897715e-05,
9510086: 9.645825310936763e-05,
9510161: 7.810961938278568e-05,
9510209: 0.0002740406070460393,
9511080: 6.39173535337445e-05,
9512077: 0.00012160816409595138,
9512181: 5.719703497479072e-05,
9512196: 5.3770273625945336e-05,
9512219: 8.134832831093459e-05,
9601036: 6.140005950728017e-05,
9602010: 4.118449447145758e-05,
9210127: 7.108556846016292e-05,
9304104: 4.2736999127762965e-05,
9506156: 3.84555738104841e-05,
9508107: 3.8025200525874865e-05,
9509106: 3.728606518015493e-05,
9512129: 3.9094128315940544e-05,
9512178: 3.852660961472316e-05,
9602049: 7.472970506363676e-05,
9602064: 8.595524035660634e-05,
9602097: 3.893483402546229e-05,
9602118: 3.778215133245248e-05,
9512053: 4.8334336438208607e-05,
9602030: 4.9906085650911974e-05,
9602070: 0.00012176535581329592,
9602098: 3.266154075928229e-05,
9602120: 5.9762786131003666e-05,
9404060: 3.4358635342828856e-05,
9511108: 4.762186373405857e-05,
9511157: 3.845964065472208e-05,
9602102: 4.0288176659599585e-05,
9603037: 4.9285132851835734e-05,
9511204: 3.700324394425885e-05,
9512078: 5.678577809235497e-05,
9509151: 5.491177673541792e-05,
9511026: 5.347356504920565e-05,
9511088: 5.132755764464783e-05,
9601029: 0.0002093335479703299,
9601162: 3.7965005963157465e-05,
9602052: 0.00015340323039475854,
9603142: 0.00017046177599824782,
9309110: 5.453728741100115e-05,
9403054: 3.5273971072842986e-05,
9406090: 4.080232863555191e-05,
9412117: 3.773908692993837e-05,
9502012: 3.642581675109451e-05,
9510055: 3.5377927736986934e-05,
9605036: 3.627457682892038e-05,
9603081: 7.474906646608609e-05,
9603127: 7.869089154033341e-05,
9604198: 5.922014272521655e-05,
9605150: 7.843363371633636e-05,
9605199: 6.055361385316679e-05,
9406178: 3.8651582999552314e-05,
9510227: 7.063529558226644e-05,
9512062: 0.0001291039591622848,
9603042: 5.7956362286211764e-05,
9602167: 5.9753400033835425e-05,
9509132: 5.337896319403826e-05,
9608039: 4.316159713789603e-05,
9608079: 4.5230864385249845e-05,
9604097: 4.660036397041347e-05,
9605154: 3.964122540755369e-05,
9608111: 5.833756867441128e-05,
9605131: 4.437325381111243e-05,
9605200: 5.673531293413303e-05,
9606044: 4.4156452370838314e-05,
9606086: 4.295342301385521e-05,
9607020: 3.834180140629664e-05,
9607139: 4.442271285129262e-05,
9608024: 0.0001368802168847366,
9608047: 4.016107681087968e-05,
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9909070: 5.66733648544322e-05,
1033: 6.470112839863674e-05,
2226: 3.7753848825730145e-05,
9702056: 4.02396104524618e-05,
9907080: 3.5963767699866515e-05,
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9405029: 0.00017629478685081618,
9705117: 6.640444123410376e-05,
9902067: 3.6653821008933096e-05,
9903091: 3.603074795628299e-05,
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1003: 3.609584202158727e-05,
9912242: 4.2266099665188664e-05,
9909127: 3.613486919238631e-05,
9910269: 3.287012421412987e-05,
9912160: 5.783736746141119e-05,
1004: 3.278032307884335e-05,
9810131: 3.831295105749166e-05,
1005: 3.185227491149061e-05,
9311183: 4.707970471208913e-05,
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9909030: 5.116927455262915e-05,
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208020: 0.00012832935170821824,
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9309093: 3.7443854508556776e-05,
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9204036: 5.360914893852281e-05,
9205040: 3.9221439001736746e-05,
9211130: 3.591239537948529e-05,
9212060: 5.347440703204982e-05,
9301026: 4.969155535788793e-05,
...}
in_degrees = [GCC.in_degree(x) for x in ranks]
out_degrees = [GCC.out_degree(x) for x in ranks]
fig = plt.figure(figsize=(12, 8))
plt.scatter(in_degrees, ranks.values(), c=out_degrees, marker=".")
plt.colorbar(label="out degree")
plt.xscale("log")
plt.yscale("log")
plt.plot(sorted(in_degrees), predicted_rank(sorted(in_degrees), q), "k-")
plt.xlabel("in degree")
plt.ylabel("PageRank")
Text(0, 0.5, 'PageRank')
new_G = nx.directed_configuration_model(in_degrees, out_degrees)
cm_ranks = nx.pagerank(new_G, 1 - q)
cm_in_degrees = [new_G.in_degree(x) for x in cm_ranks]
cm_out_degrees = [new_G.out_degree(x) for x in cm_ranks]
fig = plt.figure(figsize=(12, 8))
plt.scatter(cm_in_degrees, cm_ranks.values(), c=cm_out_degrees, marker=".")
plt.colorbar(label="out degree")
plt.xscale("log")
plt.yscale("log")
plt.plot(sorted(cm_in_degrees), predicted_rank(sorted(cm_in_degrees), q), "k-")
plt.xlabel("in degree")
plt.ylabel("PageRank")
Text(0, 0.5, 'PageRank')
knn_G = nx.average_degree_connectivity(G, source="out", target="in")
knn_CM_G = nx.average_degree_connectivity(new_G, source="out", target="in")
k1, k2 = np.mean(np.array(in_degrees)), np.mean(np.array(in_degrees) ** 2)
plt.figure(figsize=(10, 7))
plt.loglog(knn_CM_G.keys(), knn_CM_G.values(), "x", alpha=0.6, label="CM")
plt.loglog(knn_G.keys(), knn_G.values(), "o", label="data")
plt.hlines(
k2 / k1, np.min(in_degrees), np.max(in_degrees), colors="k", linestyles="dashed"
)
plt.legend()
plt.ylabel("avg deg connectivity")
plt.xlabel("node index")
Text(0.5, 0, 'node index')
9.2. Explicit walks#
## Code imported from https://github.com/kerighan/graph-walker
!pip install pybind11
!pip install graph-walker
Requirement already satisfied: pybind11 in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (2.10.4)
[notice] A new release of pip is available: 23.1.2 -> 24.0
[notice] To update, run: python3.9 -m pip install --upgrade pip
Requirement already satisfied: graph-walker in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (1.0.6)
Requirement already satisfied: networkx in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (from graph-walker) (3.1)
Requirement already satisfied: numpy in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (from graph-walker) (1.24.2)
Requirement already satisfied: scipy in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (from graph-walker) (1.11.4)
Requirement already satisfied: pybind11 in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (from graph-walker) (2.10.4)
Requirement already satisfied: scikit-learn in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (from graph-walker) (1.2.2)
Requirement already satisfied: joblib>=1.1.1 in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (from scikit-learn->graph-walker) (1.2.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/maxime/.pyenv/versions/3.9.13/envs/python_39/lib/python3.9/site-packages (from scikit-learn->graph-walker) (3.1.0)
[notice] A new release of pip is available: 23.1.2 -> 24.0
[notice] To update, run: python3.9 -m pip install --upgrade pip
9.2.1. Example of walks on ER graph#
import walker
G = nx.fast_gnp_random_graph(3, 0.01, directed=True)
# generate random walks
X = walker.random_walks(G, n_walks=5, walk_len=4)
Random walks - T=0.01s
/Users/maxime/.pyenv/versions/venv_xgi/lib/python3.9/site-packages/walker/preprocessing.py:14: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.
A = nx.adjacency_matrix(G).astype(np.float32)
import walker
# create a random graph
G = nx.fast_gnp_random_graph(5000, 0.01, directed=True)
# generate random walks
X = walker.random_walks(G, n_walks=50, walk_len=25)
X
Random walks - T=0.42s
array([[ 0, 1911, 2053, ..., 2134, 2732, 1716],
[ 1, 296, 2523, ..., 2513, 3966, 4028],
[ 2, 913, 4681, ..., 39, 1086, 2493],
...,
[4997, 1329, 2644, ..., 1620, 1700, 3994],
[4998, 4284, 2588, ..., 4960, 3712, 1938],
[4999, 3788, 2327, ..., 1841, 1846, 1166]], dtype=uint32)
X.shape
(250000, 25)
# generate random walks with restart probability alpha
X = walker.random_walks(G, n_walks=50, walk_len=25, alpha=0.1)
Random walks - T=0.21s
# you can generate random walks from specified starting nodes
X = walker.random_walks(G, n_walks=50, walk_len=25, start_nodes=[0, 1, 2])
Random walks - T=0.37s
X.shape
(150, 25)
X
array([[ 0, 1194, 756, ..., 3094, 515, 3884],
[ 1, 1648, 4677, ..., 439, 4949, 4433],
[ 2, 4367, 2157, ..., 3108, 3536, 4873],
...,
[ 0, 3623, 2567, ..., 3588, 1385, 2995],
[ 1, 4908, 991, ..., 2406, 1443, 4028],
[ 2, 193, 244, ..., 3028, 3107, 943]], dtype=uint32)
9.3. Return times#
9.3.1. On ER graph#
def return_time(walk, verbose=False):
starting_node = walk[0]
return_times = [t for (t, n) in enumerate(walk) if n == starting_node]
if verbose == True:
print(return_times)
if len(return_times) > 1:
return np.diff(return_times)
else:
return [len(walk)];
print(set(dict(G.in_degree).values()))
{29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76}
k_nodes = [n for n in G.nodes if G.in_degree(n) < 40]
len(k_nodes)
314
walks = walker.random_walks(G, n_walks=50, walk_len=50, alpha=0, start_nodes=k_nodes)
Random walks - T=0.41s
collected_returns = []
for walk in tqdm(list(walks)):
collected_returns.extend(list(return_time(walk)))
100%|█████████████████████████████████| 15700/15700 [00:00<00:00, 322064.39it/s]
plt.hist(collected_returns, bins=range(0, 100, 2))
print(np.mean(collected_returns))
plt.xlabel("return time")
plt.ylabel("# occurrences")
49.82103050761098
Text(0, 0.5, '# occurrences')
k_nodes = [n for n in G.nodes if G.in_degree(n) > 60]
len(k_nodes)
375
walks = walker.random_walks(G, n_walks=50, walk_len=50, alpha=0, start_nodes=k_nodes)
collected_returns = []
for walk in tqdm(list(walks)):
collected_returns.extend(list(return_time(walk)))
Random walks - T=0.45s
100%|█████████████████████████████████| 18750/18750 [00:00<00:00, 333584.44it/s]
plt.hist(collected_returns, bins=np.linspace(40, 60, 20))
print(np.mean(collected_returns))
plt.xlabel("return time")
plt.ylabel("# occurrences")
49.69537624660018
Text(0, 0.5, '# occurrences')
9.3.2. On scale-free graph#
from scipy.stats import zipf
a = 2.5
seq = zipf.rvs(a, loc=1, size=10000)
if np.sum(seq) % 2 != 0:
seq[0] = seq[0] + 1
G = nx.configuration_model(seq)
print(set(dict(G.degree).values()))
{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 140, 14, 15, 13, 16, 18, 17, 20, 21, 22, 23, 24, 19, 26, 27, 25, 29, 30, 31, 32, 33, 28, 35, 36, 37, 38, 39, 40, 41, 42, 172, 45, 44, 47, 48, 51, 71, 73, 79, 82, 372}
k_nodes = [n for n in G.nodes if G.degree(n) < 4]
len(k_nodes)
8758
walks = walker.random_walks(G, n_walks=30, walk_len=400, alpha=0, start_nodes=k_nodes)
collected_returns = []
for walk in tqdm(list(walks)):
collected_returns.extend(list(return_time(walk)))
Random walks - T=0.43s
100%|████████████████████████████████| 262740/262740 [00:05<00:00, 51857.25it/s]
plt.hist(collected_returns, bins=np.linspace(1, 200, 30))
print(np.mean(collected_returns))
plt.xlabel("return time")
plt.ylabel("# occurrences")
113.56366376153973
Text(0, 0.5, '# occurrences')
k_nodes = [n for n in G.nodes if G.degree(n) > 80]
len(k_nodes)
6
walks = walker.random_walks(G, n_walks=30, walk_len=400, alpha=0, start_nodes=k_nodes)
collected_returns = []
for walk in tqdm(list(walks)):
collected_returns.extend(list(return_time(walk)))
Random walks - T=0.04s
100%|██████████████████████████████████████| 180/180 [00:00<00:00, 19097.81it/s]
plt.hist(collected_returns, bins=range(0, 200, 2))
print(np.mean(collected_returns))
plt.xlabel("return time")
plt.ylabel("# occurrences")
59.16145181476846
Text(0, 0.5, '# occurrences')
9.3.3. On WS graph#
Theory predicts
\[p_0(t) \sim t^{-D/2} \quad t\ll t_1\]
\[p_0(t) \sim e^{-(p^2t)^{1/3}} \quad t_1\ll t\]
with \(t_1 \sim 1/p^2\), where \(p\) is the shortcut probability
n_exp = 6
N = 10**n_exp
G = nx.watts_strogatz_graph(N, 6, 0.01)
walks = walker.random_walks(
G,
n_walks=20,
walk_len=10000000,
alpha=0,
start_nodes=random.sample(list(G.nodes), 5),
)
Random walks - T=7.62s
collected_returns = []
for walk in tqdm(list(walks)):
collected_returns.extend(list(return_time(walk)))
100%|█████████████████████████████████████████| 100/100 [00:47<00:00, 2.10it/s]
aa, bb = np.histogram(collected_returns, bins=np.logspace(0, 6, 100), density=True)
plt.loglog(bb[:-1], aa, "--o")
print(np.mean(collected_returns))
plt.xlabel("return time")
plt.ylabel("# occurrences")
393998.3420509291
Text(0, 0.5, '# occurrences')
p = 0.01
def long_time_theory_ws(x, p, scaled=10e-7):
return scaled * np.exp(-np.power((p**2) * (x - x[0]), 0.33))
def short_time_theory_ws(x, D, scaled=10e-1):
return scaled * np.power(x, -D / 2)
plt.plot(bb[:-1], aa, "--o")
th_bins = np.linspace(1 / (p**2), 10**n_exp, 100)
plt.plot(th_bins, long_time_theory_ws(th_bins, p))
th_bins = np.linspace(1, 1 / (p**2), 100)
plt.plot(th_bins, short_time_theory_ws(th_bins, 3.5))
plt.yscale("log")
plt.xscale("log")
plt.vlines(1 / (p**2), 0, 1, color="k", linestyle="dashed")
plt.xlabel("return time")
plt.ylabel("# occurrences")
Text(0, 0.5, '# occurrences')
9.4. Graph Coverage#
def discovered_nodes(walk, steps=10):
L = []
if isinstance(steps, int):
bins = range(0, len(walk), steps)
for i in steps:
L.append(len(set(walk[:i])))
return L;
9.4.1. ER graph#
G = nx.fast_gnp_random_graph(1000, 0.001)
walks = walker.random_walks(G, n_walks=1, walk_len=1000, alpha=0)
Random walks - T=0.01s
bins = np.logspace(0, 5, 15)
bins = list(map(int, bins))
covs = [discovered_nodes(walk, steps=bins) for walk in walks]
import pandas as pd
df = pd.DataFrame(covs)
plt.loglog(bins, df.mean(), "o--")
[<matplotlib.lines.Line2D at 0x2babf4d30>]
9.4.2. WS model#
for n_exp in tqdm([3, 4, 5, 6]):
N = 10**n_exp
G = nx.watts_strogatz_graph(N, 6, 0.01)
walks = walker.random_walks(
G,
n_walks=20,
walk_len=10000000,
alpha=0,
start_nodes=random.sample(list(G.nodes), 5),
)
bins = np.logspace(0, 6, 20)
bins = list(map(int, bins))
covs = [discovered_nodes(walk, steps=bins) for walk in walks]
import pandas as pd
df = pd.DataFrame(covs)
plt.loglog(bins[4:], df.mean()[4:], "o-", label=n_exp)
plt.legend()
plt.vlines(1 / p**2, 0, 10**6, color="k", linestyles="dashed")
0%| | 0/4 [00:00<?, ?it/s]/Users/maxime/.pyenv/versions/venv_xgi/lib/python3.9/site-packages/walker/preprocessing.py:14: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.
A = nx.adjacency_matrix(G).astype(np.float32)
Random walks - T=2.87s
25%|███████████▎ | 1/4 [00:09<00:27, 9.12s/it]/Users/maxime/.pyenv/versions/venv_xgi/lib/python3.9/site-packages/walker/preprocessing.py:14: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.
A = nx.adjacency_matrix(G).astype(np.float32)
Random walks - T=2.91s
50%|██████████████████████▌ | 2/4 [00:18<00:18, 9.25s/it]/Users/maxime/.pyenv/versions/venv_xgi/lib/python3.9/site-packages/walker/preprocessing.py:14: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.
A = nx.adjacency_matrix(G).astype(np.float32)
Random walks - T=3.00s
75%|█████████████████████████████████▊ | 3/4 [00:28<00:09, 9.56s/it]/Users/maxime/.pyenv/versions/venv_xgi/lib/python3.9/site-packages/walker/preprocessing.py:14: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.
A = nx.adjacency_matrix(G).astype(np.float32)
Random walks - T=7.46s
100%|█████████████████████████████████████████████| 4/4 [00:47<00:00, 11.86s/it]
<matplotlib.collections.LineCollection at 0x2bae5b9a0>
