Proceedings of the 2004 ACM symposium on Applied computing
Mining hidden community in heterogeneous social networks
Proceedings of the 3rd international workshop on Link discovery
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Merging multiple criteria to identify suspicious reviews
Proceedings of the fourth ACM conference on Recommender systems
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Aggregating content and network information to curate twitter user lists
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
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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.