Open AccessSocial Coordinates
Author(s) -
Phuong N. H. Phạm,
Fredrik Erlandsson,
S. Felix Wu
Publication year - 2017
Publication title -
kth publication database diva (kth royal institute of technology)
Language(s) - English
Resource type - Conference proceedings
ISBN - 978-1-4503-4828-7
DOI - 10.1145/3036290.3036298
Subject(s) - computer science , graph , similarity (geometry) , theoretical computer science , scalability , euclidean space , recommender system , dimension (graph theory) , euclidean distance , benchmark (surveying) , noise (video) , data mining , algorithm , artificial intelligence , mathematics , machine learning , combinatorics , database , geodesy , image (mathematics) , geography
We present a scalable framework to embed nodes of a large social network into an Euclidean space such that the proximity between embedded points reflects the similarity between the corresponding graph nodes. Axes of the embedded space are chosen to maximize data variance so that the dimension of the embedded space is a parameter to regulate noise in data. Using recommender system as a benchmark, empirical results show that similarity derived from the embedded coordinates outperforms similarity obtained from the original graph-based measures
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