link prediction across networks by biased cross-network sampling

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$or node correspone&ample' t$e keywords o% t$eir p"blis$ed paper


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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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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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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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: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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*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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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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Instead o% a*eraging o*er all nodes in t$e so"rce network' we comp"te e&pected *al"distrib"tion

link prediction across networks by biased cross-network sampling

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