A Chernoff Bound for Random Walks on Expander Graphs
SIAM Journal on Computing
Proceedings of the 11th international conference on World Wide Web
Approximating Aggregate Queries about Web Pages via Random Walks
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Approximate Query Processing: Taming the TeraBytes
Proceedings of the 27th International Conference on Very Large Data Bases
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Random sampling from a search engine's index
Proceedings of the 15th international conference on World Wide Web
Random walks in peer-to-peer networks: algorithms and evaluation
Performance Evaluation - P2P computing systems
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Individual behavior and social influence in online social systems
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Get lost: facilitating serendipitous exploration in location-sharing services
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Anonymizing social networks: A generalization approach
Computers and Electrical Engineering
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As online social networking emerges, there has been increased interest to utilize the underlying social structure as well as the available social information to improve search. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. To this end, we introduce sampling based algorithms to quickly approximate quantities of interest from the vicinity of a user's social graph. We then introduce and analyze variants of this basic scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.