Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
SIAM Journal on Computing
Follow the reader: filtering comments on slashdot
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Friends and foes: ideological social networking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mopping up: modeling wikipedia promotion decisions
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Fast Counting of Triangles in Large Real Networks without Counting: Algorithms and Laws
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
Spectral Counting of Triangles in Power-Law Networks via Element-Wise Sparsification
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
Controversial users demand local trust metrics: an experimental study on Epinions.com community
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
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We consider social networks in which links are associated with a sign; a positive (negative) sign indicates friendship (animosity) between the connected nodes. Recent work studies such large online signed networks by applying theories that stem from the notion of social balance. Computing the social balance of a signed network requires counting the distinct configurations of the signed edges within all possible triangles that appear in the network. A naive algorithm for such counting would require time that is cubic to the total number of nodes; such an algorithm is infeasible for large signed networks that are generated from online applications. In this paper, we present an efficient spectral algorithm that computes approximate counts of the signed-triangle configurations. The essence of the algorithm lies in associating the eigenvalues of the adjacency matrix of a signed network with its signed-triangle configurations. Our experiments demonstrate that our algorithm introduces only a small error in the computed quantities while, at the same time, it achieves significant computational speedups.