Patterns of Cascading Behavior in Large Blog Graphs

By Jure Leskovec, Mary McGlohon, Christos Faloutsos, Natalie S. Glance, Matthew F. Hurst. SDM, 2007

512 citations recorded by the source corpus.

How do blogs cite and influence each other? How do such links evolve? Does the popularity of old blog posts drop exponentially with time? These are some of the questions that we address in this work. Blogs (weblogs) have become an important medium of information because of their timely publication, ease of use, and wide availability. In fact, they often make headlines, by discussing and discovering evidence about political events and facts. Often blogs link to one another, creating a publicly available record of how information and influence spreads through an underlying social network. Aggregating links from several blog posts creates a directed graph which we analyze to discover the patterns of information propagation in blogspace, and thereby understand the underlying social network. Here we report some surprising findings of the blog linking and information propagation structure, after we analyzed one of the largest available datasets, with 45, 000 blogs and ≈ 2.2 million blog-postings. Our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e6e7ea1190505daa05f36ef9bbfe269cbb5de0ec

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In Statistical and Nonlinear Physics

Citations in the corpus

Information diffusion and external influence in networks (2012, background): “A rich set of models has been developed that all try to describe different mechanisms by which the contagion spreads from the infected to an uninfected node [8, 10, 22, 24, 29].”
Tracing information flow on a global scale using Internet chain-letter data (2008, background): “It plays a fundamental role in settings that include the spread of technological innovations (1, 2), word-of-moutheffectsinmarketing(3–5),thespreadofnewsandopinion (6–8),collectiveproblem-solving(9,10),andsamplingmethodsfor hiddenpopulations(11,12).”
Rise and fall patterns of information diffusion: model and implications (2012, background): “5 as discovered by earlier work on read data: real bloggers [22], and response to mails by Einstein and Darwin [2].”
Spread of Hate Speech in Online Social Media (2018, background): “However, there are several works that looks into diffusion of fake news [26, 37, 41], Linkedin [3], retweet cascade [8, 9, 18, 37], rumours [11, 17, 20, 22, 45] and Tumblr [1, 2, 7, 42].”
Statistical properties of community structure in large social and information networks (2008, background): “40 Blog citation network (6 months of data) [113] Blog-nat06all 32,384 315,713 0.”
Maximizing Social Influence in Nearly Optimal Time (2012, background): “Many subsequent works have studied the nature of diffusion in online social networks, using empirical data to estimate influence probabilities and infer network topology; see [29, 19, 28].”
Diffusion of Lexical Change in Social Media (2012, background): “n inducing a network of connections from a cascade of “infection” events. 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]. We share the goal of reconstructing a latent ”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, background): “74 Authors-to-papers network of hep-th [116] AtP-DBLP 615,678 944,456 0.”
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TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015, methodology): “In this work, we also adopt the idea of graph transfer but focus on non-streaming scenarios for cross-network node classification.”
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