Cascading Behavior in Large Blog Graphs

2007

283 citations recorded by the source corpus.

Citations in the corpus

• Patterns of temporal variation in online media (2011, background): “Patterns of human attention [34, 35], popularity [24, 30] and response dynamics [6, 10] have been extensively studied.”
• Can cascades be predicted? (2014, background): “Such cascades have been identified in settings including blogging [1, 13, 21], e-mail [12, 22], product recommendation [20], and social sites such as Facebook and Twitter [9, 18].”
• Diffusion of Lexical Change in Social Media (2012, background): “For example, Gomez-Rodriguez et al. reconstruct networks based on cascades of events [23], such as the use of a URL on a blog [24] or the appearance of a short textual phrase [25].”
• Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter (2011, background): “…ABSTRACT There is a widespread intuitive sense that different kinds of infor­mation spread differently on-line, but it has been dif.cult to eval­uate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environ­ment.”
• Patterns of Cascading Behavior in Large Blog Graphs (2007, methodology): “We also built a generative model for cascades, which was removed for space and may be found in [15].”
• Cost-effective outbreak detection in networks (2007, methodology): “Let us replicate all elements according to their cost, and assign weights to them according to their benefit cost ratio, i.e., for all replica s $ of element s , set weight δ $ s = δ s /c ( s ).”
• A framework for quantitative analysis of cascades on networks (2010, background): “At most, researchers have visualized the shape of cascades [17] or enumerated their commonly observed patterns [1].”
• Modeling Information Diffusion in Implicit Networks (2010, background): “It is difficult to obtain large scale diffusion data and to identify and track on a large scale the elements, such as recommendations [25], links [27], [28], tags [8], [7], topics [3], phrases or " memes " [26], that spread and propagate through networks.”
• The structure of information pathways in a social communication network (2008, background): “Examples of this include cascades of e-mail recommendations for products [21], cascades of references among bloggers [3, 13, 23], the spread of e-mail chain letters [24], and the search for distant targets in a social network [8, 29].”
• Topic modeling with network regularization (2008, background): “On the other hand, social network analysis (SNA) focuses on the topology structure of a network [11, 13, 1, 12], addressing questions such as “what the diameter of a network is [13]”, “how a network evolves [13, 1]”, “how information diffuses on the network [9, 14]”, and “what are the communities on”
• Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks (2010, unknown): “…viral product recommendation (Leskovec, Adamic, and Huberman 2006), global properties of the spread of email messages (Wu et al. 2004; Liben-Nowell and Kleinberg 2008) and blog posts (Leskovec et al. 2007b), and identification of influential blogs (Gruhl and Liben-nowell 2004; Leskovec et al. 2007a).”