Prominence in Social Networks
Prominence in Social Networks
in this work, we consider prominence computation in social networks. Instead of only considering relationships between people, we ask a different questions: What about the links between the objects people create? How are they connected? People collaborate on objects and these objects form natural groups: like research collaborations, research areas or venues they appear in. We first use data mining algorithms to find the natural groupings between objects. These groupings show us that prominent people tend to belong to prominent groups with prominent objects.
Using this intuition, we compute prominence using an iterative algorithm. We show that our algorithm beats in performance using many different measures of performance many well known algorithms like hits, pagerank and various centrality measures for many different data sets like the Internet movie database (IMDB), Enron mail data and Academic research collaborations (DBLP).
The preliminary version of this work appears in ICWSM 2011. This work appears in TKDD 7(4), 2013 (ACM DL).
Software is available at github.com/rpitrust/prominence/tree/master/iHypR
Tuesday, March 1, 2011