StructInf: Mining structural influence from social streams
StructInf: Mining structural influence from social streams
Social influence is a fundamental issue in social network analysis and has attracted tremendous attention with the rapid growth of online social networks. However, existing research mainly focuses on studying peer influence. This paper introduces a novel notion of structural influence and studies how to efficiently discover structural influence patterns from social streams. We present three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process. Experiments on a big microblogging dataset show that the proposed sampling algorithms can achieve a 10 times speedup compared to the exact influence pattern mining algorithm, with an average error rate of only 1.0%. The extracted structural influence patterns have many applications. We apply them to predict retweet behavior, with performance being significantly improved.
Social Networks
Zhang, Jing
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Tang, Jie
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Yuanyi, Zhong
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Mo, Yuchen
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Li, Juanzi
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Hall, Wendy
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Sun, Jimeng
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10 February 2017
Zhang, Jing
d6afba24-5cbe-4ff1-967f-486e75419b99
Tang, Jie
69c44bae-b1fa-45eb-a01d-3ac5b00fa749
Yuanyi, Zhong
7bec645f-3c6c-4255-9f58-012b724a406d
Mo, Yuchen
5c625f2e-4ddb-43e4-a261-1ac2c3acb819
Li, Juanzi
ae072821-8a13-45f2-88ef-817e1d474e9e
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Sun, Jimeng
575ab043-f427-44a1-90f2-cc8d8eb33c8b
Zhang, Jing, Tang, Jie, Yuanyi, Zhong, Mo, Yuchen, Li, Juanzi, Hall, Wendy and Sun, Jimeng
(2017)
StructInf: Mining structural influence from social streams.
In Thirty-First AAAI Conference on Artificial Intelligence.
AAAI Press..
Record type:
Conference or Workshop Item
(Paper)
Abstract
Social influence is a fundamental issue in social network analysis and has attracted tremendous attention with the rapid growth of online social networks. However, existing research mainly focuses on studying peer influence. This paper introduces a novel notion of structural influence and studies how to efficiently discover structural influence patterns from social streams. We present three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process. Experiments on a big microblogging dataset show that the proposed sampling algorithms can achieve a 10 times speedup compared to the exact influence pattern mining algorithm, with an average error rate of only 1.0%. The extracted structural influence patterns have many applications. We apply them to predict retweet behavior, with performance being significantly improved.
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More information
Published date: 10 February 2017
Venue - Dates:
Thirty-First AAAI Conference on Artificial Intelligence, , San Francisco, United States, 2017-02-04 - 2017-02-09
Keywords:
Social Networks
Identifiers
Local EPrints ID: 427968
URI: http://eprints.soton.ac.uk/id/eprint/427968
PURE UUID: 0f82ea89-8a91-4eb0-a346-4aba8786502c
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Date deposited: 06 Feb 2019 17:30
Last modified: 16 Mar 2024 02:33
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Contributors
Author:
Jing Zhang
Author:
Jie Tang
Author:
Zhong Yuanyi
Author:
Yuchen Mo
Author:
Juanzi Li
Author:
Jimeng Sun
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