Properties of on-line social systems

Grabowski, Andrzej; Kruszewska, Natalia; Kosiński, R. A.

doi:10.1140/epjb/e2008-00379-2

Cited by 31 publications

(15 citation statements)

References 23 publications

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“…A pioneering work was done by Castronova, who traveled in a virtual world called "Norrath" and performed preliminary analysis of its economy [4]. Recently, there have been also efforts in the field of computational social sciences from a complex network perspective [5][6][7][8][9][10][11]. In addition to its scientific potentials, virtual worlds could act as nice places for real social activities, such as marketing [12][13][14], and provide opportunities for players to make real money [15].…”

Section: Introductionmentioning

confidence: 99%

Statistical properties of user activity fluctuations in virtual worlds

Yang

Xie

et al. 2017

Chaos, Solitons & Fractals

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User activity fluctuations reflect the performance of online society. We investigate the statistical properties of 1-min user activity time series of simultaneously online users inhabited in 95 independent virtual worlds. The number of online users exhibits clear intraday and weekly patterns due to human's circadian rhythms and week cycles. Statistical analysis shows that the distribution of absolute activity fluctuations has a power-law tail for 44 virtual worlds with an average tail exponent close to 2.15. The partition function approach unveils that the absolute activity fluctuations possess multifractal features for all the 95 virtual worlds. For the sample of 44 virtual worlds with power-law tailed distributions of the absolute activity fluctuations, the width of singularity ∆α is negatively correlated with the maximum activity (p-value=0.070) and the time to the maximum activity (p-value=0.010). The negative correlations are not observed for neither the other 51 virtual worlds nor the whole sample of the 95 virtual worlds. In addition, numerical experiments indicate that both temporal structure and large fluctuations have influence on the multifractal spectrum. We also find that the temporal structure has stronger impact on the singularity width than large fluctuations. [Chaos, Solitons & Fractals 105, 271-278 (2017)]

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Section: Introductionmentioning

confidence: 99%

Statistical properties of user activity fluctuations in virtual worlds

Yang

Xie

et al. 2017

Chaos, Solitons & Fractals

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“…As of December 15, 2016, the Stack Overflow data dump contains 24,158,127 posts that have at least one code snippet, 73,934,777 individual code snippets, and 46,663 total tags. Despite the large amount of data, there is a severe long-tailed phenomenon that is common in many online communities [13]. The distributions of code-snippet length and number of tags per post are of particular importance to our problem.…”

Section: Statistics and Data Correctionmentioning

confidence: 99%

A language-agnostic model for semantic source code labeling

Gelman¹,

Hoyle²,

Moore³

et al. 2018

Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis

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Code search and comprehension have become more difficult in recent years due to the rapid expansion of available source code. Current tools lack a way to label arbitrary code at scale while maintaining up-to-date representations of new programming languages, libraries, and functionalities. Comprehensive labeling of source code enables users to search for documents of interest and obtain a high-level understanding of their contents. We use Stack Overflow code snippets and their tags to train a language-agnostic, deep convolutional neural network to automatically predict semantic labels for source code documents. On Stack Overflow code snippets, we demonstrate a mean area under ROC of 0.957 over a long-tailed list of 4,508 tags. We also manually validate the model outputs on a diverse set of unlabeled source code documents retrieved from Github, and obtain a top-1 accuracy of 86.6%. This strongly indicates that the model successfully transfers its knowledge from Stack Overflow snippets to arbitrary source code documents.

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“…Although the random graph is an important type of network structure, it has been shown that realistic social networks usually have different structures [1,24]. As indicated in [7], different types of network structure can lead to different dynamics of influence diffusion.…”

Section: Realistic Graphsmentioning

confidence: 99%

“…It has been shown that a lot of realistic large networks have the scale-free degree distribution 4 , such as the World Wide Web [2], the Internet [18], online social networks [24] and so on. In order to generate a scale-free network, we adopt the algorithm for the Barabási-Albert model as follows [4]: initially, a complete graph with a small number ðm 0 Þ of nodes is constructed; at each time step, one node and mð6 m 0 Þ edges are added into the graph such that the added node is connected to m randomlyselected existing nodes; the probability that an old node i is connected to the new node is set to K i = P j K j , where K j is the degree of node j; the process is terminated until the network contains N nodes.…”

Section: Realistic Graphsmentioning

confidence: 99%