TY - CONF
T1 - StructInf
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
N1 - Funding Information:
Acknowledgments. The work is supported by the National High-tech R&D Program (No. 2015AA124102, No.2014AA015204), NSSFC (13&ZD190, 12&ZD220), National Key R&D Program(No.2016YFB1000702), NSFC (61272137, 61202114, 61532021), Online Education Research Center, Ministry of Education (2016ZD102), and an Royal Society-Newton Advanced Fellowship Award. Ji-meng Sun was supported by the National Science Foundation, award IIS-#1418511 and CCF-#1533768, Children’s Healthcare of Atlanta, CDC I-SMILE project, Google Faculty Award, AWS Research Award, Microsoft Azure Research Award and UCB. Jie Tang is the corresponding author.
Publisher Copyright:
© Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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.
UR - http://www.scopus.com/inward/record.url?scp=85030485718&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030485718&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85030485718
SP - 73
EP - 79
Y2 - 4 February 2017 through 10 February 2017
ER -