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 id : |
913
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| type : |
PhD_Thesis
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| dateandtime : |
2019-12-16 11:00:00
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| duration : |
120 min.
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Recommended duration for PhD thesis is 90 minutes, for other seminar types, it is 60 minutes. The duration specified here is used to reserve the room.
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| place : |
A105
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| departmental : |
yes
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| title : |
IMPROVED LINK PREDICTION FOR LOCATION BASED SOCIAL
NETWORKS WITH NOVEL NEW FEATURES AND CONTEXTUAL
FEATURE REDUCTION
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| author : |
AHMET ENGIN BAYRAK
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| supervisors : |
PROF.DR.FARUK POLAT
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Supervisors field is applicable especially for a Thesis Defense
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| company : |
Computer Engineering Dept. Middle East Technical Univ.
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| country : |
Turkey
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| abstract : |
High penetration of broadband Internet access has made a revolution on the web usage,
where users have become content generators rather than just consuming. People
started to communicate, interact, maintain relationship and share data (image, video,
note, location, etc.) with their acquaintances through varying online social network
sites which are the key factors of that internet usage revolution. Online social networks
with location sharing and interaction between people are called Location Based
Social Networks (LBSNs). To use and benefit more from social networks, real life
social links (friendship, acquaintanceship) should be represented well on them. Link
Prediction problem has a motivation of studying social network evolution and trying
to predict future possible links for representing the real-life relations better. In
this work, we studied a comprehensive feature set which combines topological features
with features calculated from temporal interaction data on LBSNs. We proposed
novel new features which are calculated by using time, category and common friend
details of candidates and their social interaction in LBSNs. In addition, we proposed
an effective feature reduction mechanism which helps to determine best feature subset in two steps. Contextual feature clustering is applied to remove redundant features
and then a non-monotonic selection of relevant features from the calculated clusters
are done by a custom designed Genetic algorithm. Results depict that both new
features and the proposed feature reduction method improved link prediction performance
for LBSNs.
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final
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