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In many social networks, several different link relations will exist betweenthe same set of users. Additionally, attribute or textual information will beassociated with those users, such as demographic details or user-generatedcontent. For many data analysis tasks, such as community finding and datavisualisation, the provision of multiple heterogeneous types of user data makesthe analysis process more complex. We propose an unsupervised method forintegrating multiple data views to produce a single unified graphrepresentation, based on the combination of the k-nearest neighbour sets forusers derived from each view. These views can be either relation-based orfeature-based. The proposed method is evaluated on a number of annotatedmulti-view Twitter datasets, where it is shown to support the discovery of theunderlying community structure in the data.

Producing a Unified Graph Representation from Multiple Social Network Views