Open AccessProceedings Article
StructInf: Mining structural influence from social streams
Jing Zhang,Jie Tang,Yuanyi Zhong,Yuchen Mo,Juanzi Li,Guojie Song,Wendy Hall,Jimeng Sun +7 more
- 01 Jan 2017
- pp 73-80
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TL;DR: This paper introduces a novel notion of structural influence and studies how to efficiently discover structural influence patterns from social streams by presenting three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process.
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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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Citations
Topological Recurrent Neural Network for Diffusion Prediction
Jia Wang,Vincent W. Zheng,Zemin Liu,Kevin Chen-Chuan Chang +3 more
- 01 Nov 2017
TL;DR: In this paper, a novel topological recurrent neural network (Topo-LSTM) was proposed to model dynamic directed acyclic graphs (DAGs) for information diffusion prediction.
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Ranking Users in Social Networks with Higher-Order Structures
Huan Zhao,Xiaogang Xu,Yangqiu Song,Dik Lun Lee,Zhao Chen,Han Gao +5 more
- 25 Apr 2018
TL;DR: This paper proposes a novel framework, motif-based PageRank (MPR), to incorporate higher-order structures into conventional PageRank computation, and conducts extensive experiments in three real-world networks to show that MPR can significantly improve the effectiveness of PageRank for ranking users in social networks.
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A survey of Big Data dimensions vs Social Networks analysis.
TL;DR: This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s).
Ranking Users in Social Networks with Motif-Based PageRank
TL;DR: Zhang et al. as discussed by the authors proposed a novel framework, motif-based PageRank (MPR), to incorporate higher-order relations into the conventional PageRank computation, where motifs are subgraphs consisting of a small number of nodes.
51
Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis
Belal Abdullah Hezam Murshed,Suresha Mallappa,Jemal H. Abawajy,Mufeed Ahmed Naji Saif,Hasib Daowd Esmail Al-ariki,Hudhaifa Mohammed Abdulwahab +5 more
TL;DR: A comprehensive survey and taxonomy of Short Text Topic Modeling (STTM) algorithms for short text topic modeling is presented in this paper , which includes qualitative and quantitative study of the STTM algorithms, as well as analyses of the various strengths and drawbacks of STTM techniques.
References
Maximizing the Spread of Influence through a Social Network
TL;DR: The problem of finding the most influential nodes in a social network is NP-hard as mentioned in this paper, and the first provable approximation guarantees for efficient algorithms were provided by Domingos et al. using an analysis framework based on submodular functions.
A generalization of sampling without replacement from a finite universe.
D. G. Horvitz,D. J. Thompson +1 more
TL;DR: In this paper, two sampling schemes are discussed in connection with the problem of determining optimum selection probabilities according to the information available in a supplementary variable, which is a general technique for the treatment of samples drawn without replacement from finite universes when unequal selection probabilities are used.
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A 61-million-person experiment in social influence and political mobilization
Robert M. Bond,Christopher J. Fariss,Jason J. Jones,Adam D. I. Kramer,Cameron Marlow,Jaime E. Settle,James H. Fowler +6 more
TL;DR: Results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people.
2.7K
gSpan: graph-based substructure pattern mining
Xifeng Yan,Jiawei Han +1 more
- 09 Dec 2002
TL;DR: A novel algorithm called gSpan (graph-based substructure pattern mining), which discovers frequent substructures without candidate generation by building a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label.
Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study
TL;DR: People’s happiness depends on the happiness of others with whom they are connected, providing further justification for seeing happiness, like health, as a collective phenomenon.
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