random graphs - the university of edinburgh · random graphs • most basic, most unstructured...
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RandomGraphs
SocialandTechnologicalNetworks
RikSarkar
UniversityofEdinburgh,2016.
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Graph
• V:setofnodes• n=|V|:Numberofnodes
• E:setofedges• m=|E|:Numberofedges
• Ifedgea-bexists,thenaandbarecalledneighbors
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Graph
• Howmanyedgescanagraphhave?
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Graph
• Howmanyedgescanagraphhave?
• InbigO?
✓n
2
◆OR
n(n� 1)
2
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Graph
• Howmanyedgescanagraphhave?
✓n
2
◆OR
n(n� 1)
2
O(n2)
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Randomgraphs
• Mostbasic,mostunstructuredgraphs• Formsabaseline– Whathappensinabsenceofanyinfluences
• Socialandtechnologicalforces
• Manyrealnetworkshavearandomcomponent– Manythingshappenwithoutclearreason
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Erdos–RenyiRandomgraphs
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Erdos–RenyiRandomgraphs
• n:numberofverTces• p:probabilitythatanyparTcularedgeexists
• TakeVwithnverTces• Considereachpossibleedge.AddittoEwithprobabilityp
G(n, p)
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Expectednumberofedges
• Expectedtotalnumberofedges
• Expectednumberofedgesatanyvertex
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Expectednumberofedges
• Expectedtotalnumberofedges
• Expectednumberofedgesatanyvertex
�n2
�p
(n� 1)p
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Expectednumberofedges
• For
• Theexpecteddegreeofanodeis:?
p =c
n� 1
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IsolatedverTces
• HowlikelyisitthatthegraphhasisolatedverTces?
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IsolatedverTces
• HowlikelyisitthatthegraphhasisolatedverTces?
• WhathappenstothenumberofisolatedverTcesaspincreases
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Terminologyofhighprobability
• Somethinghappenswithhighprobabilityif
• Wherepoly(n)meansapolynomialinn• Apolynomialinnisconsideredreasonably‘large’
p �✓1� 1
poly(n)
◆
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ProbabilityofIsolatedverTces
• IsolatedverTcesare
• Likelywhen:
• Unlikelywhen:
• Let’sdeduce
p < lnnn
p > lnnn
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UsefulinequaliTes
✓1 +
1
x
◆x
e
✓1� 1
x
◆x
1
e
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Unionbound
• ForeventsA,B,C…
• Pr[AorBorC...]≤Pr[A]+Pr[B]+Pr[C]+...
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• Theorem1:• If
• Thentheprobabilitythatthereexistsanisolatedvertex
• Thusforlargen,w.h.pthereisnoisolatedvertex• ExpectednumberofisolatedverTcesisminiscule
p = (1 + ✏)lnn
n� 1
1
n✏
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• Theorem2• For
• Probabilitythatvertexvisisolated
p = (1� ✏)lnn
n� 1
� 1
(2n)1�✏
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• Theorem2• For
• Probabilitythatvertexvisisolated
• ExpectednumberofisolatedverTces:
p = (1� ✏)lnn
n� 1
� 1
(2n)1�✏
� n
(2n)1�✏=
n✏
2Polynomialinn
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Thresholdphenomenon:ProbabilityornumberofisolatedverTces
• TheTppingpoint
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Clusteringinsocialnetworks• Peoplewithmutualfriendsareodenfriends
• IfAandChaveacommonfriendB– EdgesABandBCexist
• ThenABCissaidtoformaTriad– Closedtriad:EdgeACalsoexists– Opentriad:EdgeACdoesnotexist
• Exercise:Provethatanyconnectedgraphhasatleastntriads(consideringbothopenandclosed).
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Clusteringcoefficient(cc)
• MeasureshowTghtthefriendneighborhoodsare:frequencyofclosedtriads
• cc(A)fracTonsofpairsofA’sneighborsthatarefriends
• Averagecc:averageofccofallnodes• Globalcc:raTo #closedtriads
#alltriads
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GlobalCCinERgraphs
• Whathappenswhenpisverysmall(almost0)?
• Whathappenswhenpisverylarge(almost1)?
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GlobalCCinERgraphs
• WhathappensattheTppingpoint?
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Theorem
• For
• GlobalccinERgraphsisvanishinglysmall
p = clnn
n
lim
n!1cc(G) = lim
n!1
# closed triads
# all triads
= 0
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AvgCCInrealnetworks
• Facebook(olddata)~0.6• hhps://snap.stanford.edu/data/egonets-Facebook.html
• Googlewebgraph~0.5• hhps://snap.stanford.edu/data/web-Google.html
• Ingeneral,ccof~0.2or0.3isconsidered‘high’– thatthenetworkhassignificantclustering/communitystructure