StructInf: Mining structural influence from social streams

Jing Zhang; Jie Tang; Yuanyi Zhong; Yuchen Mo; Juanzi Li; Guojie Song; Wendy Hall; Jimeng Sun

StructInf: Mining structural influence from social streams

Jing Zhang, Jie Tang, Yuanyi Zhong, Yuchen Mo, Juanzi Li, Guojie Song, Wendy Hall, Jimeng Sun

Research output: Contribution to conference › Paper › peer-review

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× 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.

Original language	English (US)
Pages	73-79
Number of pages	7
State	Published - Jan 1 2017
Externally published	Yes
Event	31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States Duration: Feb 4 2017 → Feb 10 2017

Other

Other	31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country	United States
City	San Francisco
Period	2/4/17 → 2/10/17

ASJC Scopus subject areas

Artificial Intelligence

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title = "StructInf: Mining structural influence from social streams",

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× 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.",

author = "Jing Zhang and Jie Tang and Yuanyi Zhong and Yuchen Mo and Juanzi Li and Guojie Song and Wendy Hall and Jimeng Sun",

year = "2017",

month = jan,

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language = "English (US)",

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note = "31st AAAI Conference on Artificial Intelligence, AAAI 2017 ; Conference date: 04-02-2017 Through 10-02-2017",

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T2 - 31st AAAI Conference on Artificial Intelligence, AAAI 2017

AU - Zhang, Jing

AU - Tang, Jie

AU - Zhong, Yuanyi

AU - Mo, Yuchen

AU - Li, Juanzi

AU - Song, Guojie

AU - Hall, Wendy

AU - Sun, Jimeng

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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× 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.

AB - 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× 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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Y2 - 4 February 2017 through 10 February 2017

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