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VERSION:2.0
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BEGIN:VEVENT
SUMMARY:Quantum Link Prediction in Complex Networks
DTSTART:20211217T090000Z
DTEND:20211217T110000Z
DTSTAMP:20260627T032240Z
UID:0c2ad62e-a81c-480e-9439-ac336984aeae
SEQUENCE:7
CREATED:20211213T155130Z
DESCRIPTION:Password: 392699Meeting ID: 836 1514 8258Predicting new l
 inks in physical\, biological\, social\, or technological networks has a s
 ignificant scientific and societal impact. Network based link prediction m
 ethods utilize topological patterns in a network to infer new or unobserve
 d links. Here\, we propose a quantum algorithm for link prediction\, QLP\,
  which uses quantum walks to infer unknown links based on even and odd len
 gth paths. By sampling new links from quantum measurements\, QLP avoids th
 e need to explicitly calculate all pairwise scores in the network. We stud
 y the complexity of QLP and discuss in which cases one may achieve a polyn
 omial speedup over classical link prediction methods. Furthermore\, tests 
 with real-world datasets show that QLP is at least as precise as state-of-
 the-art classical link prediction methods\, both in cross-validation tests
  and in the prediction of experimentally verified protein-protein interact
 ions.
LAST-MODIFIED:20211213T164438Z
LOCATION:Online
URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/quantum-link-prediction-in
 -complex-networks/
X-ALT-DESC;FMTTYPE=text/html:<p data-block-key="ckc7k">Password: 392699<b
 r/></p><p data-block-key="earm3">Meeting ID: 836 1514 8258</p><p data-
 block-key="9ukdr">Predicting new links in physical\, biological\, social\,
  or technological networks has a significant scientific and societal impac
 t. Network based link prediction methods utilize topological patterns in a
  network to infer new or unobserved links. Here\, we propose a quantum alg
 orithm for link prediction\, QLP\, which uses quantum walks to infer unkno
 wn links based on even and odd length paths. By sampling new links from qu
 antum measurements\, QLP avoids the need to explicitly calculate all pairw
 ise scores in the network. We study the complexity of QLP and discuss in w
 hich cases one may achieve a polynomial speedup over classical link predic
 tion methods. Furthermore\, tests with real-world datasets show that QLP i
 s at least as precise as state-of-the-art classical link prediction method
 s\, both in cross-validation tests and in the prediction of experimentally
  verified protein-protein interactions.<br/><br/></p>
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