Bridge analysis in a Social Internetworking Scenario

Buccafurri, Francesco; Foti, Vincenzo Daniele; Lax, Gianluca; Nocera, Antonino; Ursino, Domenico

doi:10.1016/j.ins.2012.10.021

Cited by 43 publications

(28 citation statements)

References 21 publications

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“…To perform this analysis, we discretize degree by applying the logarithmic binning function reported in Table 2. The choice of the logarithmic binning function allows us to obtain almost equal-width bins (Milojević , 2010) due to the well-known power law distribution of node degree (Buccafurri, Foti, Lax, Nocera, & Ursino, 2013;Lu & Wang, 2014).…”

Section: Privacy Settingmentioning

confidence: 99%

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Comparing Twitter and Facebook user behavior: Privacy and other aspects

Buccafurri

Lax

Nicolazzo

et al. 2015

Computers in Human Behavior

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Section: Privacy Settingmentioning

confidence: 99%

“…This table shows that the average degree of Twitter accounts is much higher than that of Facebook. However, because it is well-know that degree in social networks follows a power law distribution (Buccafurri et al, 2013;Lu & Wang, 2014), we need to better investigate this results.…”

Section: Friend Distributionmentioning

confidence: 99%

Comparing Twitter and Facebook user behavior: Privacy and other aspects

Buccafurri

Lax

Nicolazzo

et al. 2015

Computers in Human Behavior

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“…The dataset used in this experimental campaign is a synthetic graph combined with real-life biometric data. It is well known that the degree of social-network graphs follows a power-law distribution [31], [32], [33]. This can be obtained by using the Barabási-Albert model [34], one of the most famous algorithms for generating random scale-free networks using preferential attachment.…”

Section: Resultsmentioning

confidence: 99%

Contrasting False Identities in Social Networks by Trust Chains and Biometric Reinforcement

Buccafurri

Lax

Migdal

et al. 2017

2017 International Conference on Cyberworlds (CW)

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Abstract-Fake identities and identity theft are issues whose relevance is increasing in the social network domain. This paper deals with this problem by proposing an innovative approach which combines a collaborative mechanism implementing a trust graph with keystroke-dynamic-recognition techniques to trust identities. The trust of each node is computed on the basis of neighborhood recognition and behavioral biometric support. The model leverages the word of mouth propagation and a settable degree of redundancy to obtain robustness. Experimental results show the benefit of the proposed solution even if attack nodes are present in the social network.

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“…For the model presented in this paper, the specific scores can be real preferences given by users and which can be automatically extracted by using social network analysis [17,52], be fixed to model concrete scenarios (as Section 6 considers), or simply be random.…”

Section: Definition 2 {Social Choice Function) a Social Choice Functmentioning

confidence: 99%

Evaluating social choice techniques into intelligent environments by agent based social simulation

Serrano

Moncada

Garijo

et al. 2014

Information Sciences

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The primary hypothesis stated by this paper is that the use of social choice theory in Ambient Intelligence systems can improve significantly users' satisfaction when accessing shared resources. A research methodology based on agent based social simulations is employed to support this hypothesis and to evaluate these benefits. The result is a sixfold contribution summarized as follows. Firstly, several considerable differences between this application case and the most prominent social choice application, political elections, have been found and described. Secondly, given these differences, a number of metrics to evaluate different voting systems in this scope have been proposed and formalized. Thirdly, given the presented application and the metrics proposed, the performance of a number of well known electoral systems is compared. Fourthly, as a result of the performance study, a novel voting algorithm capable of obtaining the best balance between the metrics reviewed is introduced. Fifthly, to improve the social welfare in the experiments, the voting methods are combined with cluster analysis techniques. Finally, the article is complemented by a free and open-source tool, VoteSim, which ensures not only the reproducibility of the experimental results presented, but also allows the interested reader to adapt the case study presented to different environments.

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Bridge analysis in a Social Internetworking Scenario

Cited by 43 publications

References 21 publications

Comparing Twitter and Facebook user behavior: Privacy and other aspects

Comparing Twitter and Facebook user behavior: Privacy and other aspects

Contrasting False Identities in Social Networks by Trust Chains and Biometric Reinforcement

Evaluating social choice techniques into intelligent environments by agent based social simulation

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