Understanding latent interactions in online social networks
By Jing Jiang, Christo Wilson, Xiao Wang, Wenpeng Sha, Peng Huang, Yafei Dai, Ben Y. Zhao. ACM/SIGCOMM Internet Measurement Conference, 2010
304 citations recorded by the source corpus.
Subfields: Communication, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/ec683f2f06172f134d018975d0bb7ef9b6d7eb32
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Communication
- Aram Galstyan
- Xiao Wang
- Johan Ugander
- Cameron A. Marlow
- Greg Ver Steeg
- Thomas M. Lento
- Lars Backstrom
- Brian Karrer
In Statistical and Nonlinear Physics
- Jure Leskovec
- Lars Backstrom
- Jon M. Kleinberg
- Eytan Bakshy
- Emilio Ferrara
- Johan Ugander
- Xiao Wang
- Cameron A. Marlow
Citations in the corpus
- Center of Attention: How Facebook Users Allocate Attention across Friends (2011, background): “(See also Jiang et al. (2010) for further discussion of this contrast in on-line social networks.)”
- Uncovering social network sybils in the wild (2011, background): “Figure 10: Degree distribution of normal users (from [7]) and targets of friend requests from Sybils.”
- Latent Space Model for Multi-Modal Social Data (2015, background): “However, a number of recent studies makes it increasingly clear that focusing only on network properties while discarding rich contextual information limits our comprehensive understanding of complex social systems dynamics [52, 36, 4, 56, 18, 32, 49, 37, 20, 25, 27, 2].”
- Sybil Attacks and Their Defenses in the Internet of Things (2014, background): “In [73], it shows that the Sybil users rarely establish social connections with other Sybil users in RenRen.”
- The Anatomy of the Facebook Social Graph (2011, background): “Two recent studies of Renren [15] and MSN messenger [14] included 42 million and 180 million individuals respectively.”
- Understanding user behavior in online social networks: a survey (2013, unknown): “Jiang et al. [2] study latent interactions based on the crawled data of Renren, the largest OSN provider in China.”
- Detecting and characterizing social spam campaigns (2010, background): “studied user browsing behavior in the context of a 42 million user measurement of the Renren social network, through detailed OSN logs of user profile visits [26].”