Network denoising in social media

Huiji Gao; Xufei Wang; Jiliang Tang; Huan Liu

doi:10.1145/2492517.2492547

Network denoising in social media

Huiji Gao, Xufei Wang, Jiliang Tang, Huan Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
Publisher	Association for Computing Machinery
Pages	564-571
Number of pages	8
ISBN (Print)	9781450322409
DOIs	https://doi.org/10.1145/2492517.2492547
State	Published - 2013
Event	2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 - Niagara Falls, ON, Canada Duration: Aug 25 2013 → Aug 28 2013

Publication series

Name	Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

Other

Other	2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
Country/Territory	Canada
City	Niagara Falls, ON
Period	8/25/13 → 8/28/13

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

Access to Document

10.1145/2492517.2492547

Cite this

Gao, H., Wang, X., Tang, J., & Liu, H. (2013). Network denoising in social media. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 (pp. 564-571). (Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013). Association for Computing Machinery. https://doi.org/10.1145/2492517.2492547

Network denoising in social media. / Gao, Huiji; Wang, Xufei; Tang, Jiliang et al.
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. Association for Computing Machinery, 2013. p. 564-571 (Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gao, H, Wang, X, Tang, J & Liu, H 2013, Network denoising in social media. in Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Association for Computing Machinery, pp. 564-571, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Niagara Falls, ON, Canada, 8/25/13. https://doi.org/10.1145/2492517.2492547

Gao H, Wang X, Tang J, Liu H. Network denoising in social media. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. Association for Computing Machinery. 2013. p. 564-571. (Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013). doi: 10.1145/2492517.2492547

Gao, Huiji ; Wang, Xufei ; Tang, Jiliang et al. / Network denoising in social media. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. Association for Computing Machinery, 2013. pp. 564-571 (Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013).

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abstract = "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.",

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AB - 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.

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Network denoising in social media

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