Link Prediction with Social Vector Clocks
|Title:||Link Prediction with Social Vector Clocks||Authors:||Lee, Conrad
|Permanent link:||http://hdl.handle.net/10197/6618||Date:||14-Aug-2013||Abstract:||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 the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||ACM||Copyright (published version):||2013 the Authors||Keywords:||Recommender Systems;Social networks;Vector clocks;Link prediction;Online algorithms||DOI:||10.1145/2487575.2487615||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining||Conference Details:||19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 11-14 August, Chicago United States|
|Appears in Collections:||Insight Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.