Name
Abstract | ||
|---|---|---|
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 relation-based 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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2464464.2464471 | Proceedings of the 5th Annual ACM Web Science Conference |
Keywords | DocType | Volume |
multiple social network view,data visualisation,unified graph representation,data analysis task,multiple heterogeneous type,unsupervised method,multiple data view,underlying community structure,analysis process,user data,community finding,data integration,social network analysis,social media | Conference | abs/1301.5809 |
Citations | PageRank | References |
15 | 0.66 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Derek Greene | 1 | 297 | 24.34 |
| Pádraig Cunningham | 2 | 3086 | 218.37 |