BEGIN:VCALENDAR
VERSION:2.0
PRODID:ECMLPKDD-MB
BEGIN:VEVENT
DTSTAMP;TZID=Europe/Dublin:20180826T200000
UID:_ecmlpkdd_DAMI-D-18-00061
DTSTART;TZID="Europe/Dublin":20180911T120000
DTEND;TZID="Europe/Dublin":20180911T122000
LOCATION:Hogan Mezz 2
TRANSP:TRANSPARENT
SEQUENCE:1
DESCRIPTION:Due to the scale and complexity of todays' social networks, it becomes infeasible to mine them with traditional approaches. A possible solution to reduce such scale and complexity is to produce a compact (lossy) version of the network that represents its major properties. This task is known as graph summarization, which is the subject of this research. Our focus is on time-evolving graphs, a more complex scenario where the dynamics of the network also should be taken into account. We address this problem using tensor decomposition, which enables us to capture the multi-way structure of the time-evolving network. This property is unique and is impossible to obtain with other approaches such as matrix factorization. Experimental evaluation on five real world networks implies promising results demonstrating that tensor decomposition is quite useful for summarizing dynamic networks.
SUMMARY:Dynamic Graph Summarization: A Tensor Decomposition Approach
CLASS:PUBLIC
END:VEVENT
END:VCALENDAR