The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Fast incremental proximity search in large graphs
Proceedings of the 25th international conference on Machine learning
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering via random walk hitting time on directed graphs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Power-Law Distributions in Empirical Data
SIAM Review
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Assessing and ranking structural correlations in graphs
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring temporal effects for location recommendation on location-based social networks
Proceedings of the 7th ACM conference on Recommender systems
A general framework for geo-social query processing
Proceedings of the VLDB Endowment
Mobility and social networking: a data management perspective
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
The emerging location-based social network (LBSN) services not only allow people to maintain cyber links with their friends, but also enable them to share the events happening on them at different locations. The geo-social correlations among event participants make it possible to quantify mutual user influence for various events. Such a quantification of influence could benefit a wide spectrum of real-life applications such as targeted advertising and viral marketing. In this paper, we perform an in-depth analysis of the geo-social correlations among LBSN users at event level, based on which we address two problems: user influence evaluation and influential events discovery. To capture the geo-social closeness between LBSN users, we propose a unified influence metric. This metric combines a novel social proximity measure named penalized hitting time, with a geographical weight function modeled by power law distribution. We propose two approximate algorithms, namely global iteration (GI) and dynamic neighborhood expansion (DNE), to efficiently evaluate user influence with tight theoretical error bounds. We then adopt the sampling technique and the threshold algorithm to support efficient retrieval of top-K influential events. Extensive experiments on both real-life and synthetic LBSN data sets confirm that the proposed algorithms are effective, efficient, and scalable.