Nowadays useful information. Flicker is an online photo and

Nowadays
online networking sites provide user platforms to interact with each other.  Communicating and sharing information in online
social networks such as Facebook, twitter or flicker are an important part of peoples
ever day life, which leads to huge amount of data each day. The important
question is how to use this data in order to extract useful information. Flicker
is an online photo and video sharing platform with social networking features,
founded in 2004. In 2008, Flicker claims to have more than 3 billion images.
Flicker allows users to create friendship and add their favorite photos to
their profiles.

One
of the amazing features of social networks is how they play a fundamental role in
Information propagation. Word of mouth is widely been used as a marketing
instrument to spread content and ideas about products through population widely
and quickly. In this paper authors try to answers questions about how widely and
quickly information spread in social networks? Is this information spreading locally
or globally? For example if an image is popular in a certain region will it be
added as favorite in different parts of the network? How quickly information
propagation happens? Do people discover their favorite photos through their
links (friends)?

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In
order to answer these questions Cha et al. collect the information of flicker
from 2.5 million users and 33 million links between and they try to capture the
dynamics of information propagation for 104 consecutive days. First they
randomly select users and by following their friends links to get a “snowball”
sample of the Flickr social network. The authors examine different network structural
properties such as nodes in and out degrees, diameter, path length, Clustering
Coefficient, etc. The flicker networks exhibits a small-world network structure
which implicates that information spreading could happen in flicker over short
network paths.

In
this paper authors focus on the fan popularity of pictures and try to investigate
how fast and widely users add photos as their favorites. First they compare the
most popular local and global list of photos while they assuming that if photos
widely spread throughout the networks there will be a high similarity between
the two. They discover no overlap between the local and global hotlists in the
one-hop neighborhood which shows strong locality across most of the popularity
levels. Later authors investigate how the distances from the uploaders affect
the distribution of fans of photos. By taking into account the different information
propagation mechanisms through flicker (Search results, External links, Word-of-mouth
etc.), the authors find that Social cascade plays
an important role in information spreading through Flicker. Cha et al do not consider
Flicker as a general online social network and suggest apply the same research to
general cases and find out if the results changes for much larger networks?