Projected gradient methods for linearly constrained problems
Mathematical Programming: Series A and B
Convex Separable Minimization Subject to Bounded Variables
Computational Optimization and Applications
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Convex Optimization
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with trust and distrust relationships
Proceedings of the third ACM conference on Recommender systems
Scalable learning of collective behavior based on sparse social dimensions
Proceedings of the 18th ACM conference on Information and knowledge management
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Discovering Overlapping Groups in Social Media
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
The impact of network structure on breaking ties in online social networks: unfollowing on twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Nonlinear Programming: Theory and Algorithms
Nonlinear Programming: Theory and Algorithms
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Social media expands the ways people communicate with each other. On a popular social media website, a user typically has hundreds of contacts (or friends) on average. As a person's social network grows, friend management is increasingly important for effective communications. Often, one can only afford to maintain close friendship in a small scale due to limited time and other resources. In other words, the majority of one's connections are so-so friends and do not hold strong influence on the user. One approach resorts to network denoising, by which unimportant connections are removed as noise. We study the challenges of network denoising in social media and how we can leverage a variety of social media information to denoise the links. We formulate the network denoising task as an optimization problem, and show the efficacy of our network denoising approach and its scalability experimentally in the domain of behavior inference.