Walking in Facebook: A Case Study of Unbiased Sampling of OSNs

By Minas Gjoka, Maciej Kurant, Carter T. Butts, Athina Markopoulou. 2010 Proceedings IEEE INFOCOM, 2010

743 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a4903aea23b66bf0680430f52fe4f5fa88379580

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Citations in the corpus

Web Data Extraction, Applications and Techniques: A Survey (2012, background): “The correctness of this procedure derives from the fact that each Facebook user was uniquely identified by a numerical ID ranging from 0 and 232 − 1.”
Sampling Content from Online Social Networks (2015, background): “With the advent of large networked systems, there have been several studies on sampling large networks such as the Web-graph [Rusmevichientong et al. 2001] and social networks [Frank 1978; Gjoka et al. 2010; Granovetter 1976; Leskovec and Faloutsos 2006].”
Comparing Community Structure to Characteristics in Online Collegiate Social Networks (2008, background): “Other studies of Facebook have typically obtained data either through surveys [2] or through various forms of automated sampling [12], and thus they tend to contain missing nodes and links that can strongly impact the resulting graph structures and analyses.”
Network resilience (2020, neutral): “Serval sampling methods, such as random walk [853], breadth-first sampling [854], and scaling-down sampling [851], has been pro-”
The Anatomy of the Facebook Social Graph (2011, background): “[23], that strict power-law models are inappropriate for Facebook’s degree distribution.”
Who is tweeting on Twitter: human, bot, or cyborg? (2010, methodology): “Inspired by [15, 18], we randomly select five users as seeds.”
The socialbot network: when bots socialize for fame and money (2011, methodology): “First, we generated 64-bit integers at random, but with a range that is reduced to the known ID ranges used by Facebook [15].”
Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? (2012, methodology): “Inspired by [32], [33], we randomly select five users as seeds.”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, methodology): “This approach avoids known biases of other methods such as snowball and breadth-first sampling, which rely on the selection of an initial group of users (Gjoka et al. 2010; Morstatter et al. 2013).”
Random walks and diffusion on networks (2016, methodology): “This method has been used for sampling in peer-to-peer (P2P) and online social networks [42, 388, 389].”
Social Structure of Facebook Networks (2011, neutral): “Received 7 October 2011 Received in revised form 3 December 2011 Available online 19 December 2011”
When Social Influence Meets Item Inference (2015, background): “ 19] with a higher probability that the algorithms will select high-degree nodes and their neighbors. On the other hand, Uniform Sampling (US) introduces a much smaller bias, but it is less practical [13]. Recently, the gap between US and RW has been bridged with a proposed scheme named the Metropol”