Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Approximating s-t minimum cuts in Õ(n2) time
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Syntactic clustering of the Web
Selected papers from the sixth international conference on World Wide Web
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying and Filtering Near-Duplicate Documents
COM '00 Proceedings of the 11th Annual Symposium on Combinatorial Pattern Matching
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Journal of the ACM (JACM)
Graph sparsification by effective resistances
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the forty-first annual ACM symposium on Theory of computing
A sketch-based distance oracle for web-scale graphs
Proceedings of the third ACM international conference on Web search and data mining
Approaching Optimality for Solving SDD Linear Systems
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
A general framework for graph sparsification
Proceedings of the forty-third annual ACM symposium on Theory of computing
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Spectral sparsification via random spanners
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
Identifying same wavelength groups from twitter: a sentiment based approach
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
ASCOS: an asymmetric network structure COntext similarity measure
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Estimating the relative utility of networks for predicting user activities
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Scalable similarity estimation in social networks: closeness, node labels, and random edge lengths
Proceedings of the first ACM conference on Online social networks
Call me maybe: understanding nature and risks of sharing mobile numbers on online social networks
Proceedings of the first ACM conference on Online social networks
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Previous research has suggested that people who are in the same social circle exhibit similar behaviors and tastes. The rise of social networks gives us insights into the social circles of web users, and recommendation services (including search engines, advertisement engines, and collaborative filtering engines) provide a motivation to adapt recommendations to the interests of the audience. An important primitive for supporting these applications is the ability to quantify how connected two users are in a social network. The shortest-path distance between a pair of users is an obvious candidate measure. This paper introduces a new measure of "affinity" in social networks that takes into account not only the distance between two users, but also the number of edge-disjoint paths between them, i.e. the "robustness" of their connection. Our measure is based on a sketch-based approach, and affinity queries can be answered extremely efficiently (at the expense of a one-time offline sketch computation). We compare this affinity measure against the "approximate shortest-path distance", a sketch-based distance measure with similar efficiency characteristics. Our empirical study is based on a Hotmail email exchange graph combined with demographic information and Bing query history, and a Twitter mention-graph together with the text of the underlying tweets. We found that users who are close to each other - either in terms of distance or affinity - have a higher similarity in terms of demographics, queries, and tweets.