Researchers and practitioners increasingly are gaining access
to data on explicit social networks. For example, telecommunications
and technology firms record data on consumer
networks (via phone calls, emails, voice-over-IP, instant messaging),
and social-network portal sites such as MySpace,
Friendster and Facebook record consumer-generated data
on social networks. Inference for fraud detection [5, 3, 8],
marketing [9], and other tasks can be improved with learned
models that take social networks into account and with collective
inference [12], which allows inferences about nodes
in the network to affect each other. However, these socialnetwork
graphs can be huge, comprising millions to billions
of nodes and one or two orders of magnitude more links.
This paper studies the application of collective inference
to improve prediction over a massive graph. Faced initially
with a social network comprising hundreds of millions of
nodes and a few billion edges, our goal is: to produce an
approximate consumer network that is orders of magnitude
smaller, but still facilitates improved performance via collective
inference. We introduce a sampling technique designed
to reduce the size of the network by many orders of magnitude,
but to keep linkages that facilitate improved prediction
via collective inference.
In short, the sampling scheme operates as follows: (1)
choose a set of nodes of interest; (2) then, in analogy to
snowball sampling [14], grow local graphs around these nodes,
adding their social networks, their neighbors’ social networks,
and so on; (3) next, prune these local graphs of edges
which are expected to contribute little to the collective inference;
(4) finally, connect the local graphs together to form
a graph with (hopefully) useful inference connectivity.
We apply this sampling method to assess whether collective
inference can improve learned targeted-marketing models
for a social network of consumers of telecommunication
services. Prior work [9] has shown improvement to the learning
of targeting models by including social-neighborhood
information—in particular, information on existing customers
in the immediate social network of a potential target. However,
the improvement was restricted to the “network neighbors”,
those targets linked to a prior customer thought to
be good candidates for the new service. Collective inference
techniques may extend the predictive influence of existing
customers beyond their immediate neighborhoods. For the
present work, our motivating conjecture has been that this
influence can improve prediction for consumers who are not
strongly connected to existing customers. Our results show
that this is indeed the case: collective inference on the approximate
network enables significantly improved predictive
performance for non-network-neighbor consumers, and for
consumers who have few links to existing customers.
In the rest of this extended abstract we motivate our approach,
describe our sampling method, present results on
applying our approach to a large real-world target marketing
campaign in the telecommunications industry, and finally
discuss our findings.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc