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Actor Identification in Implicit Relational Data
Sources
Michael Farrugia and Aaron Quigley
Abstract.
Large scale network data sets have become increasingly accessible to
researchers. While computer networks, networks of webpages and biological net-
works are all important sources of data, it is the study of social networks that is
driving many new research questions. Researchers are finding that the popularity of
online social networking sites may produce large dynamic data sets of actor con-
nectivity. Sites such as Facebook have 250 million active users and LinkedIn 43
million active users. Such systems offer researchers potential access to rich large
scale networks for study. However, while data sets can be collected directly from
sources that specifically define the actors and ties between those actors, there are
many other data sources that do not have an explicit network structure defined. To
transform such non-relational data into a relational format two facets must be iden-
tified - the actors and the ties between the actors. In this chapter we survey a range
of techniques that can be employed to identify unique actors when inferring net-
works from non explicit network data sets. We present our methods for unique node
identification of social network actors in a business scenario where a unique node
identifier is not available. We validate these methods through the study of a large
scale real world case study of over 9 million records.
1
Introduction
Until quite recently the term “network” conjured up images of routers, cables and
gateways in a computer scientist's mind. Today however, due to the explosion of
social media web sites [6], the same word evokes images of “one's circle of friends”.
The same word, network, is used in different contexts, yet it is still referring to the
same structure. A network is the name given to a structure that connects objects
together. In the case of the physical network the objects were computers whereas in
