Rina Panigrahy, Marc Najork, and Yinglian Xie
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.
|Published in||5th ACM International Conference on Web Search and Data Mining (WSDM)|
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