Influence analysis is an important problem in social network analysis due to its impact on viral marketing and targeted advertisements. Most of the existing influence analysis methods determine the influencers in a static network with an influence propagation model based on pre-defined edge propagation probabilities. However, none of these models can be queried to find influencers in both context and time-sensitive fashion from a streaming social data. In this paper, we propose an approach to maintain real-time influence scores of users in a social stream using a topic and time-sensitive approach, while the network and topic is constantly evolving over time. We show that our approach is efficient in terms of online maintenance and effective in terms various types of real-time context- and time-sensitive queries. We evaluate our results on both social and collaborative network data sets.
|Original language||English (US)|
|Title of host publication||WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|State||Published - Feb 8 2016|
|Event||9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, United States|
Duration: Feb 22 2016 → Feb 25 2016
|Name||WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining|
|Other||9th ACM International Conference on Web Search and Data Mining, WSDM 2016|
|Period||2/22/16 → 2/25/16|
Bibliographical noteFunding Information:
This research was sponsored by the Defense Advanced Research Project Agency (DARPA) agreement number W911NF-12-C-0028, U.S. Army Research Laboratory (ARL) cooperative agreement number W911NF-09-2-0053 and IBM Ph.D. Fellowship. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the DARPA, ARL, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
© 2016 ACM.