[Research] ROOM CHANGE Lab Meeting Tomorrow!

Karen Widmaier krw at andrew.cmu.edu
Tue Apr 27 13:18:19 EDT 2010


Hello all,
The lab meeting for tomorrow, April 28 will be in NSH 1507.
Karen

-----Original Message-----
From: research-bounces at autonlab.org [mailto:research-bounces at autonlab.org]
On Behalf Of Karen Widmaier
Sent: Tuesday, April 27, 2010 12:54 PM
To: awd at cs.cmu.edu; research at autonlab.org
Subject: Re: [Research] Lab Meeting Tomorrow!

I've reserved GHC 6501 from noon until 1:30 for tomorrow's meeting.
Karen

-----Original Message-----
From: research-bounces at autonlab.org [mailto:research-bounces at autonlab.org]
On Behalf Of Artur Dubrawski
Sent: Tuesday, April 27, 2010 11:41 AM
To: research at autonlab.org
Subject: [Research] Lab Meeting Tomorrow!

Dear Autonians,

Please join Purna and I on Wednesday April 28th (tomorrow) at noon
to hear Purna's talk on "Tractable Ranking with Random Walks in Large 
Graphs".

Look below for the summary.

This will be a practice talk for the upcoming thesis defense so please come
ready to provide valuable feedback.

Karen Widmaier will confirm the location.

See you!
Artur

---
Summary: A wide variety of interesting real world applications, e.g.
friend suggestion in social networks, keyword search in databases,
web-spam detection etc. can be framed as ranking entities in a graph.
While random walk based proximity measures are popular tools for ranking,
they are hard to compute in real-world networks with millions of entities.
We will present algorithms which improve both quality and speed of
relevance search in large real world graphs. We mainly design local
algorithms, which are highly generalizable to different random walk based
measures and disk-based clustered graph representations. All our
algorithms are evaluated using various link-prediction tasks. Earlier work
has shown that different heuristics behave differently on link prediction
tasks on different graphs. In the last part of this talk, I will describe
how to justify useful link prediction heuristics, by bringing together
generative models for link formation and geometric intuitions.



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