Modeling Information Diffusion in Implicit Networks

By Jaewon Yang, Jure Leskovec. 2010 IEEE International Conference on Data Mining, 2010

584 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/54d7101cb619f6516ff19d3c2accfc4c372a99b6

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In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

On the Interpretation of Digital Trace Data in Communication and Social Computing Research (2014, background): “Krishnamurthy et al. (2008) and Java et al. (2007) derive their user categories from quantitative analyses of follower patterns, while boyd et al.’s (2010) observations about Twitter users’ ‘‘imagined audiences’’ emerge from crowdsourcing (see also Golder & Yardi, 2010; Yang & Leskovec, 2010).”
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning (2016, background): “! 5 distance metric, by employing graph diffusion [16], which improves weak similarity measures that are likely to result from noise or dropout events.”
The Structural Virality of Online Diffusion (2015, background): “Whether or not such cases appear with any meaningful frequency is, however, largely an empirical matter, and hence the utility of the metric must ultimately be evaluated in the context of real examples, which we discuss further below.”
Dynamics of information diffusion and its applications on complex networks (2016, background): “As each node in the network plays different roles in the information process, a model concerned about studying the influence of a particular node and how the influence changes over time is proposed in [231].”
Can cascades be predicted? (2014, background): “Much prior work aims to predict the volume of aggregate activity — the total number of up-votes on Digg stories [29], total hourly volume of news phrases [34], or total daily hashtag use [23].”
Understanding the Impact of Opinion Leaders’ Characteristics on Online Group Knowledge-Sharing Engagement from In-Group and Out-Group Perspectives: Evidence from a Chinese Online Knowledge-Sharing Community (2019, background): “In-group knowledge-sharing engagement New_Knowledgei,t The number of new knowledge posts generated in group i on statistical day t [51,118,120]”
Rise and fall patterns of information diffusion: model and implications (2012, background): “Analyses of epidemics, blogs, social media, propagation and the cascades they create have attracted much interest [21, 40, 18, 33, 32, 15, 37, 9, 10, 11, 20], and recently the reverse problem (‘find who started it’) [19, 36].”