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BEGIN:VEVENT
DTSTAMP:20260513T015946Z
DTSTART;VALUE=DATE-TIME:20181217T110000
DTEND;VALUE=DATE-TIME:20181217T120000
SUMMARY:Seminar: Manos Papagelis\, York University\, CA
TZID:Europe/London
UID:20181217-8a17841a66c4e3b70166e4f3674022e5@warwick.ac.uk
CREATED:20181204T131907Z
DESCRIPTION:Title: Trajectory Network Mining Abstract: Trajectory data mi
 ning has been of increasing research interest due to an abundance of mod
 ern tracking devices and a number of real-world applications. Likewise\,
  large-scale network mining has attracted significant attention as it he
 lps to reveal underlying dynamics of networks. In our research\, we are 
 interested in mining and analysis of large-scale trajectory networks - n
 etworks where the nodes are moving objects (cars\, pedestrians\, etc.) a
 nd the edges represent contacts between objects as defined by a proximit
 y threshold. In the first part of the talk\, I will discuss the problem 
 of mining group patterns in trajectories of pedestrians coming from moti
 on video analysis. We are interested in interactive analysis and explora
 tion of group dynamics\, including various semantics of group gathering 
 and dispersion. In the second part of the talk\, I will discuss methods 
 for evaluating the importance of moving objects (nodes) in a trajectory 
 network. To address these questions we devise fast and accurate methods 
 for concurrent evaluation of dynamic network metrics including node degr
 ee\, triangle membership and connected components for each moving object
 \, over time. If time allows\, I will also discuss our recent work on ne
 twork representation learning. Lately\, these methods have been shown to
  perform very well on static networks. However\, in real-world\, network
 s are continuously evolving. I will discuss a random-walk based method f
 or effectively learning representations of evolving networks.
LOCATION:MB 0.08
CATEGORIES:
LAST-MODIFIED:20181204T131907Z
ORGANIZER;CN=Sara Kalvala:
END:VEVENT
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