The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social network document ranking
Proceedings of the 10th annual joint conference on Digital libraries
Hierarchical parallel algorithm for modularity-based community detection using GPUs
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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This paper explores how information contained in the structure of the social graph can improve search result relevance on social networking websites. Traditional approaches to search include scoring documents for relevance based on a set of keywords or using the link structure across documents to infer quality and relevance. These approaches attempt to optimally match keywords to documents with little or no information about the searcher and no information about his network. This study analyzes 3.8M profile search queries from a large social networking site in conjunction with the tie structure of a 21M member social graph. The key finding is that a measure of social distance, when used in conjunction with standard search relevance methods, improves the ordering of profiles in search results.