Assessing the Evolution of Google+ in Its First Two Years

González, Roberto; Motamedi, Reza; Rejaie, Reza; Cuevas, Antonio

doi:10.1109/tnet.2015.2433792

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…This would make Google+ the largest OSN in number of users, followed by Facebook and Sina Weibo (a OSN that operates in China). In a previous study 15 , we collected a large-scale dataset including: (i) the connectivity graph of the Largest Connected Component of Google+, including around 400 million users; and (ii) the public activity registered in Google+ during a period of two years, since its release in June 2011 until July 2013, accounting with a total of 541 million posts, 1 billion likes, 140 million reshares, and 408 million comments. At the time of the dataset collection, Google+ had a reportedly number of registered users over half a billion.…”

Section: Contributionsmentioning

confidence: 99%

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks

Azcorra

Chiroque

Anta

et al. 2018

Sci Rep

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Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.

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Section: Contributionsmentioning

confidence: 99%

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks

Azcorra

Chiroque

Anta

et al. 2018

Sci Rep

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“…Recent years social network has become an important form of networks. Many kinds of network evolutonary process have been studied [14,15,16]. More and more social network users get used to acquire interested information by search engine.…”

Section: Related Workmentioning

confidence: 99%

Search engine drives the evolution of social networks

Peng

Liu

2017

Proceedings of the ACM Turing 50th Celebration Conference - China

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The search engine is tightly coupled with social networks and is primarily designed for users to acquire interested information. Specically, the search engine assists the information dissemination for social networks, i.e., enabling users to access interested contents with keywords-searching and promoting the process of contents-transferring from the source users directly to potential interested users. Accompanying such processes, the social network evolves as new links emerge between users with common interests. However, there is no clear understanding of such a \chicken-and-egg" problem, namely, new links encourage more social interactions, and vice versa. In this paper, we aim to quantitatively characterize the social network evolution phenomenon driven by a search engine. First, we propose a search network model for social network evolution. Second, we adopt two performance metrics, namely, degree distribution and network diameter. Theoretically, we prove that the degree distribution follows an intensi_ed power-law, and the network diameter shrinks. Third, we quantitatively show that the search engine accelerates the rumor propagation in social networks. Finally, based on four real-world data sets (i.e., CDBLP, Facebook, Weibo Tweets, P2P), we verify our theoretical _ndings. Furthermore, we _nd that the search engine dramatically increases the speed of rumor propagation.

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