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Ego-Centric Network Sampling in Viral
Marketing Applications
Huaiyu (Harry) Ma, Steven Gustafson, Abha Moitra, and David Bracewell
Abstract. Marketing is most successful when people spread the message within
their social network. The Internet can serve as an approximation of the spread of
messages, particularly marketing campaigns, to both measure marketing effective-
ness and provide data for influencing future efforts. However, to measure the net-
work of web sites spreading the marketing message potentially requires a massive
amount of data collection over a long period of time. Additionally, collecting data
from the Internet is very noisy and can create a false sense of precision. Therefore,
we propose to use ego-centric network sampling to both reduce the amount of data
required to collect as well as handle the inherent uncertainty of the data collected. In
this the topic chapter, we study whether the proposed ego-centric network sampling
accurately captures the network structure. We use the Stanford-Berkeley network to
show that the approach can capture the underlying structure with a minimal amount
of data.
1
Introduction
Viral marketing, or word-of-mouth marketing is successful when people take it upon
themselves to spread the “message” within their social network [14], potentially
reaching an audience much bigger than what the original marketing budget could
have obtained through more traditional means. On the Internet, a key to viral mar-
keting is to get a web site to recommend the target of the marketing message to
others, their social network as well as to their readers, who will hopefully spread the
message and react positively to the message. While the measurable Internet is only
an approximation of the real word-of-mouth spread of the message, it is reasonable
to argue that, in some domains, the Internet can serve as a coarse approximation of
the real social spread of a message [4]. Additionally, as more people use tools like
 
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