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8/9/2019 Link Prediction Across Networks by Biased Cross-Network Sampling
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Link Prediction acrossNetworks by Biased
Cross-Network SamplingICDE 20!
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Introd"ction
Goal# predict future links in a growing network wit$ t$e "se o% t$e e&istinstr"ct"re
Problem# e&isting network may be too sparse' wit$ too few links
Solution# ot$er (more densely linked) networks may be a*ailable w$ic$ s$o
linkage str"ct"re -+ e&isting networks can be "sed in conjunction with theattribute information wit$ t$e sparse networks
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Problem ,orm"lation
Cross-network transfer learning
Source Network (mature, more linkages info) G ! (", #)
Note 0. /1
Edge () E0
$arget Network (sparse, nascent) G ! (",#)
Note . /
Edge () E
Correspondence between t$e nodes in and 0is "nknown' and t$e only inw$ic$ relates t$em is t$e a*ailable attribute information at the nodes
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Problem ,orm"lation
Eac$ node in and 0is associated wit$ a set of ke%words deri*ed %rom pbot$ social networks
&ttributesassociated wit$ node ' G is feature ector s
&ttributesassociated wit$ node' Gis feature ector
s and are in t$e ector space *dof dimension d
Problem 5i*en t$e training network G ! (", #)' along wit$ its associaattributes ' determine the linksw$ic$ $a*e t$e $ig$est probability to ap%"t"re in a c"rrently e&isting target network G ! (",#)wit$ corresponding
Cross Network $ransferearning
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Problem ,orm"lation
2 main algorit$ms proposed6
Cross Network ink odel le*erage link in%ormation in t$e so"rce order to predict t$e links in t$e target network
Cross Network .iased Correction determination o% sampling weigbias' to ens"re t$at links in t$e target network w$ic$ are consistent wit$networks are gi*en m"c$ greater importance
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7lgorit$m 6 Cross Network Link8odel
9ses atent Space &pproach # related t$e network attrib"tes to t$e probpresence in t$e so"rce and target networks
In co-authorship network, attribute ector o% an a"t$or node correspone&le' t$e keywords o% t$eir p"blis$ed paper
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7lgorit$m 6 Cross Network Link8odel
7ttrib"te *ector and sare mapped to latent ector and /s in a latent
appinglinear trans%ormation . ; < and =s . ;< &s
8atri& ; . k & d> k . dimension o% t$e latent topic space
Social interaction between two nodes sand tin target network can
similarit%o% /s /t between corresponding latent ector
E&6 collaboration links between t$e a"t$ors in a co-a"t$ors$ip network can be in%esimilarity between t$e latent topic *ectors o% t$eir researc$ interests and e&pertise
?o do t$e mapping o% to we need to get ;
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7lgorit$m 6 Cross Network Link8odel
2+ Cross network knowledge which is transferred from source to tar
Combine model wit$ knowledge %rom a resampled so"rce network basedimportance o% link between nodes and denoted by Pi@
link e&ist> link does not e&ist
Pi@. weig$ts t$e importance o% sampling link ' in t$e so"rce network
2 Components of matri
1
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7lgorit$m 6 Cross Network Link8odel
Ne&t' ma&imiAe combined log-likeli$ood o% links in sourceand targetnetworklatent trans%ormation matri 13
?$"s' by knowing ;' to predict the probabilit% of a link between a pair of
=s =t . Latent *ector similarity o% nodes * sand *t
bst. 7damic-7dar %eat"re de4ned on a pair o% nodes *sand *tto capt"re t$e commontarget network
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7lgorit$m 26 Cross Network BiasCorrection
4dea# to ens"re t$at t$e links in t$e targetnetwork' w$ic$ are consistentsourcenetworks in terms o% t$e node-content relations$ip are gi*en m"c$ gimportance
$he closer two nodes are, thereleant their attributes
Nodes /1 in so"rce network are nodes in target network
?$"s' t$ey s$o"ld $a*e more sat$an in t$e resampling process
ink structure between /1 shointact to preser*e t$e links incidrele*ant nodes
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7lgorit$m 26 Cross Network BiasCorrection
:esampling process is done to6
8a&imiAe t$e consistenc%between t$e so"rce and target networks in terms o% t$associated wit$ t$eir nodes
Preser*e t$e richness of the structure o% t$e sampled network' so t$at as m"c$in%ormation as possible is a*ailable %or t$e trans%er learning process
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7lgorit$m 26 Cross Network BiasCorrection
*esampling the source network
Eac$ node is sampled according to a weighting distribution 5 . /' 2'
node set 0o% t$e source network
De4nition6 & re-sampled source network ! , 56is a stoc$astic network nodes are sampled %rom t$e node set 0o% t$e so"rce network 50according
sampling weights 5< ,ormally' a node 9 in t$e resampled network is a ranw$ic$ takes on *al"es %rom wit$ t$e probability t$at %or i . '2''n
?$"s' t$e probability Pi@o% sampling a link (* i' *@) E0in t$e re-sampling proc
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7lgorit$m 26 Cross Network BiasCorrection
al"e o% Pi@depends "pon sampling distrib"tion < ?$"s' o"r goal is to determine 5' w
t$e cross network bias' w$ile retaining t$e ric$ness o% network str"ct"re
*eleance * (G, G) between so"rce and target networks -+ consistenc%o% t$e dis"nderlying t$ese 2 networks
I% we dont consider node distrib"tion' we can simply meas"re t$e a*erage attrib"te st$e nodes o% t$e networks by a na7e de8nition6
Ne&t' generali9e na7e de8nition to meas"re rele*ance between re-sampled so"rcparameteri9ed b% node distribution 5 and target network G
0+ Cross-Network *eleance
S(' ) . similarity between t$e attrib"tes o% nodes
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7lgorit$m 26 Cross Network BiasCorrection
Instead o% a*eraging o*er all nodes in t$e so"rce network' we comp"te e&pected *al"distrib"tion