most citedIdentifying Topical Twitter Communities via User List Aggregation

14 citations · 31 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SI20131 cited

Link Prediction with Social Vector Clocks

Conrad Lee, Bobo Nick, Ulrik Brandes +1

State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve th…

cs.SI20135 cited

Producing a Unified Graph Representation from Multiple Social Network Views

Derek Greene, Pádraig Cunningham

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 use…

cs.SI20135 cited

Benchmarking community detection methods on social media data

Conrad Lee, Pádraig Cunningham

Benchmarking the performance of community detection methods on empirical social network data has been identified as critical for improving these methods. In particular, while most…

cs.SI2013

Measuring the Significance of the Geographic Flow of Music

Conrad Lee, Aaron McDaid, Pádraig Cunningham

In previous work, our results suggested that some cities tend to be ahead of others in their musical preferences. We concluded that work by noting that to properly test this claim,…

cs.SI20126 cited

Aggregating Content and Network Information to Curate Twitter User Lists

Derek Greene, Gavin Sheridan, Barry Smyth +1

Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of orga…

cs.SI201214 cited

Identifying Topical Twitter Communities via User List Aggregation

Derek Greene, Derek O'Callaghan, Pádraig Cunningham

A particular challenge in the area of social media analysis is how to find communities within a larger network of social interactions. Here a community may be a group of microblogg…