[AI Seminar] Fwd: AI Lunch -- Bryan Hooi -- September 6th, 2016

[Ellen Vitercik]

This is a reminder that this talk is tomorrow, Tuesday, September 6th.

---------- Forwarded message ----------
From: Ellen Vitercik <vitercik at cs.cmu.edu>
Date: Thu, Sep 1, 2016 at 8:18 AM
Subject: AI Lunch -- Bryan Hooi -- September 6th, 2016
To: bhooi at andrew.cmu.edu, ai-seminar-announce at cs.cmu.edu


Dear faculty and students,

We look forward to seeing you this Tuesday, September 6th, at noon in NSH
3305 for AI lunch. To learn more about the seminar and lunch, please visit
the AI Lunch webpage <http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday, Bryan Hooi <https://www.andrew.cmu.edu/user/bhooi/> will give a
talk titled "FRAUDAR: Bounding Graph Fraud in the Face of Camouflage."

*Abstract:* Given a bipartite graph of users and the products that they
review, or followers and followees, how can we detect fake reviews or
follows? Existing fraud detection methods (spectral, etc.) try to identify
dense subgraphs of nodes that are sparsely connected to the remaining
graph. Fraudsters can evade these methods using camouflage, by adding
reviews or follows with honest targets so that they look "normal". Even
worse, some fraudsters use hijacked accounts from honest users, and then
the camouflage is indeed organic.

Our focus is to spot fraudsters in the presence of camouflage or hijacked
accounts. We propose FRAUDAR, an algorithm that (a) is
camouflage-resistant, (b) provides upper bounds on the effectiveness of
fraudsters, and (c) is effective in real-world data. Experimental results
under various attacks show that FRAUDAR outperforms the top competitor in
accuracy of detecting both camouflaged and non-camouflaged fraud.
Additionally, in real-world experiments with a Twitter follower-followee
graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of
more than 4000 detected accounts, of which a majority had tweets showing
that they used follower-buying services.

This is joint work with Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin,
and Christos Faloutsos and won the "best research paper" award in KDD 2016.


Best,
Ellen and Ariel
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