Atish Das Sarma

What makes an image popular

Hundreds of thousands of photographs are uploaded to the internet every minute through various social networking and photo sharing platforms. While some images get millions of views, others are completely ignored. Even from the same users, different photographs receive different number of views. This begs the question: What makes a photograph popular? Can we predict the number of views a photograph will receive even before it is uploaded? These are some of the questions we address in this work. We investigate two key components of an image that affect its popularity, namely the image content and social context. Using a dataset of about 2.3 million images from Flickr, we demonstrate that we can reliably predict the normalized view count of images with a rank correlation of 0.81 using both image content and social cues. In this paper, we show the importance of image cues such as color, gradients, deep learning features and the set of objects present, as well as the importance of various social cues such as number of friends or number of photos uploaded that lead to high or low popularity of images.

Distributed Verification and Hardness of Distributed Approximation

We study the verification problem in distributed networks, stated as follows. Let

eTrust: understanding trust evolution in an online world

H

Estimating PageRank on graph streams

be a subgraph of a network

Ranking mechanisms in twitter-like forums

G

Fast distributed PageRank computation

where each vertex of

Representative skylines using threshold-based preference distributions

G

Arrival and departure dynamics in social networks

knows which edges incident on it are in

Distributed Random Walks

H

Randomized multi-pass streaming skyline algorithms

. We would l...