On the Bursty Evolution of Blogspace

By Ravi Kumar, Jasmine Novak, Prabhakar Raghavan, Andrew Tomkins. The Web Conference, 2003

676 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0f2bc8c3c403556bd36e369183bfb82722b533fa

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In Computer Networks and Communications

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

A framework for analysis of dynamic social networks (2006, background): “The last few years have seen a development of systematic algorithmic approaches to dynamic network analysis, mostly in the context of information networks [1, 18, 20, 19, 23, 24].”
Topic sentiment mixture: modeling facets and opinions in weblogs (2007, background): “Weblogs have been attracting increasing attentions from researchers, who consider weblogs as a suitable test bed for many novel research problems and algorithms [11, 7, 6, 15, 19].”
Information diffusion through blogspace (2004, background): “Power laws have been observed in many important real-world networks [23], including the social network defined by blog-to-blog links [22].”
Analyzing communities and their evolutions in dynamic social networks (2009, background): “…recently, there has been a growing body of work on the analysis of communities and their temporal evolution in dynamic networks [Asur et al. 2007; Kumar et al. 2003; Kumar et al. 2006; Leskovec et al. 2005; Lin et al. 2007; Palla et al. 2007; Spiliopoulou et al. 2006; Toyoda and Kitsuregawa…”
A probabilistic approach to spatiotemporal theme pattern mining on weblogs (2006, background): “They also proposed a way to discover bursty evolution of these communities [16].”
Conversations in the Blogosphere: An Analysis "From the Bottom Up" (2005, background): “As Marlow (2004), for example, writes, "The weblog medium, while fundamentally an innovation in personal publishing, has also come to engender a new form of social interaction on the web: a massively distributed but completely connected conversation covering every imaginable topic of interest" [emph”
Identifying the influential bloggers in a community (2008, background): “Emergence of communities in the network can be found frequently at a microscopic level [19].”
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks (2008, background): “More recently, there has been a growing body of work on the analysis of communities and their temporal evolution in dynamic networks [1, 8, 9, 10, 11, 16, 19, 21].”
The predictive power of online chatter (2005, background): “The structure and evolution of blogs were explored in [16] [17] [18] [1] .”
Leave a Reply: An Analysis of Weblog Comments (2006, methodology): “Extracting comments enriches the model of the social network of bloggers and readers, and may be used for enhancing studies of weblog communities and the interactions between them, including the burstiness work by Kumar et. al [8] and the in-depth analysis of a specific community done by Wei [20].”
Community Detection and Mining in Social Media (2010, methodology): “Such a scheme has been applied in (Hopcroft et al., 2004; Kumar et al., 2005; Palla et al., 2007; Asur et al., 2007).”
Temporal Networks (2011, background): “Typically, people have studied spreading events in blogs [9,29] or microblogs (like Twitter) [30,31].”
Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, background): “Kumar et al. (2003) studied the evolution of the blogosphere as a graph in terms of the change of characteristics, (such as in-degree, out-degree, strongly connected components), the change of communities, as well as the burstiness in blog community.”
Key differences between Web 1.0 and Web 2.0 (2008, neutral): “[1, 12, 14, 15, 16, 18].”
Discovering Large Dense Subgraphs in Massive Graphs (2005, methodology): “Currently, there are some tools that in practice extract small dense subgraphs of a particular form [24, 23, 15, 22, 21], but at heart these algorithms typically look for very small structures containing on the order of ten nodes.”