Producing a unified graph representation from multiple social network views

In many social networks, several different link relations will
exist between the same set of users. Additionally, attribute or
textual information will be associated with those users, such
as demographic details or user-generated content. For many
data analysis tasks, such as community finding and data visualisation,
the provision of multiple heterogeneous types of
user data makes the analysis process more complex. We propose
an unsupervised method for integrating multiple data
views to produce a single unified graph representation, based
on the combination of the k-nearest neighbour sets for users
derived from each view. These views can be either relationbased
or feature-based. The proposed method is evaluated on
a number of annotated multi-view Twitter datasets, where it is
shown to support the discovery of the underlying community
structure in the data.