Publication

Multi-Graph Regularization For Efficient Delivery Of User Generated Content In Online Social Networks

2011
Conference Papers

Abstract

We present a methodology for enhancing the delivery of user-generated content in online social networks. To this end, we first regularize the social graph via node capacity and link cost information associated with the underlying data network. We then design a technique for constructing the most efficient delivery tree over the regularized social graph. Finally, we derive an optimization algorithm for allocating the nodes' uplink capacities over the content distribution tree. Our system substantially outperforms the conventional method of flooding data over the social graph, over multiple criteria. In particular, a 100% reduction in terms of network cost and data delivery delay is registered.

Official source

https://infoscience.epfl.ch/entities/publication/29d99a1b-51e3-4c8c-a695-ac53abdcbb9c

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2011
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/29d99a1b-51e3-4c8c-a695-ac53abdcbb9c

About this result

Related concepts (32)

User-generated content

User-generated content (UGC), alternatively known as user-created content (UCC), is any form of content, such as images, videos, text, testimonials, and audio, that has been posted by users on online platforms such as social media, discussion forums and wikis. It is a product consumers create to disseminate information about online products or the firms that market them. User-generated content is used for a wide range of applications, including problem processing, news, entertainment, customer engagement, advertising, gossip, research and many more.

Social network

A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.

Regularization (mathematics)

In mathematics, statistics, finance, computer science, particularly in machine learning and inverse problems, regularization is a process that changes the result answer to be "simpler". It is often used to obtain results for ill-posed problems or to prevent overfitting. Although regularization procedures can be divided in many ways, the following delineation is particularly helpful: Explicit regularization is regularization whenever one explicitly adds a term to the optimization problem.

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Multi-Graph Regularization For Efficient Delivery Of User Generated Content In Online Social Networks

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