User interactions in social networks and their implications

By Christo Wilson, B. Boe, A. Sala, Krishna P. N. Puttaswamy, Ben Y. Zhao. 2009

1,027 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Exploring the diﬀerences between social networks based on explicit vs. implicit links among users, Wilson et al. (2009) also showed that the network constructed based on explicit links was signiﬁcantly denser than the one based on user interactions.”
Center of Attention: How Facebook Users Allocate Attention across Friends (2011, background): “…address this issue: Kossinets et al. (2008) study how link activity can lead to different pathways for information flow over multi-step paths, and Wilson et al. (2009) focus on aggregate measures for how activity is distributed, and the network structures that result from thresholding the links…”
GraphMat: High performance graph analytics made productive (2015, background): “Graph processing has become an important component of bioinformatics [17], social network analysis [21, 32], traffic engineering [31] etc.”
Analyzing facebook privacy settings: user expectations vs. reality (2011, background): “Additionally, Facebook also offers users the option of sharing photos with Networks [41].”
Graphicionado: A high-performance and energy-efficient accelerator for graph analytics (2016, neutral): “Facebook (FB) [51] 2.”
PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, background): “Although there are also other models [41, 58, 59, 82] for skewed graphs, we restrict the discussion to the power-law distribution due to space constraints.”
Web Data Extraction, Applications and Techniques: A Survey (2012, background): “Due to these reasons, it has been applied in a large number of studies about the topology and structure of Online Social Networks (see, for instance, [20,119,52,123,17]).”
Limiting the spread of misinformation in social networks (2011, methodology): “these factors into consideration we performed experiments on 4 regional network graphs obtained from Facebook that exhibit properties such as power-law degree distribution, high clustering and positive assortativity [43].”
Uncovering social network sybils in the wild (2011, background): “When all edges are considered, the degree distribution is unremarkable: it follows the same general trend that has been observed on numerous other OSNs [19].”
Random Walk Based Fake Account Detection in Online Social Networks (2017, background): “One way is to approximately obtain trust relationships between users by looking into user interactions [31], predicting tie strength [32], asking users to rate their social contacts [33], etc.”
An analysis of underground forums (2011, background): “[11] found that, for users on Facebook, 20% of their friends account for 70% of their interactions.”
SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, background): “, looking into user interactions [34], inferring tie strengths [13], and asking users to rate their social friends [33].”
Understanding user behavior in online social networks: a survey (2013, background): “Wilson et al. [1] try to find out whether social links are valid indicators of user interactions.”
Navigating the maze of graph analytics frameworks using massive graph datasets (2014, methodology): “We picked 4 different graph algorithms with varying characteristics in terms of functionality (traversal, statistics, machine learning), data per vertex, amount and type of communication, iterative and non-iterative etc.”
Direction-optimizing Breadth-First Search (2012, background): “The real social networks are taken from a variety of web crawls [6, 7, 12, 16, 21, 23, 25, 26].”
SybilSCAR: Sybil detection in online social networks via local rule based propagation (2017, background): “One way is to approximately obtain trust relationships between users by looking into user interactions [32], predicting tie strength [33], asking users to rate their social contacts [34], etc.”