Predicting the availability of users' devices in decentralized online social networks

Salve, Andrea De; Guidi, Barbara; Mori, Paolo

doi:10.1002/cpe.4390

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…-Linear predictor (LP) algorithm is a simple and intuitive linear prediction model that has been successfully used in the field of OSN [3,21]. Formally, given the set of influence scores {I t 1 (u), I t 2 (u), .…”

Section: Predictionmentioning

confidence: 99%

Predicting Influential Users in Online Social Network Groups

Salve¹,

Mori

Guidi

et al. 2021

ACM Trans. Knowl. Discov. Data

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The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount of information produced by their users, and the corresponding capacity to influence markets, politics, and society, have led both industrial and academic researchers to focus on how such systems could be influenced . While previous work has mainly focused on measuring current influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. Indeed, one of the main characteristics of OSNs is the ability of users to create different groups types, as well as to join groups defined by other users, in order to share information and opinions. In this article, we formulate the Influencers Prediction problem in the context of groups created in OSNs, and we define a general framework and an effective methodology to predict which users will be able to influence the behavior of the other ones in a future time period, based on historical interactions that occurred within the group. Our contribution, while rooted in solid rationale and established analytical tools, is also supported by an extensive experimental campaign. We investigate the accuracy of the predictions collecting data concerning the interactions among about 800,000 users from 18 Facebook groups belonging to different categories (i.e., News, Education, Sport, Entertainment, and Work). The achieved results show the quality and viability of our approach. For instance, we are able to predict, on average, for each group, around a third of what an ex-post analysis will show being the 10 most influential members of that group. While our contribution is interesting on its own and—to the best of our knowledge—unique, it is worth noticing that it also paves the way for further research in this field.

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Section: Predictionmentioning

confidence: 99%

Predicting Influential Users in Online Social Network Groups

Salve¹,

Mori

Guidi

et al. 2021

ACM Trans. Knowl. Discov. Data

Self Cite

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“…Each user selects four authorised friends who have the highest probability of being online at a specific time. They extend this model in [28] to overcome some limitations of the previous predictor.…”

Section: Related Workmentioning

confidence: 99%

Data availability improvement in peer‐to‐peer online social networks

Koohpar

Fatemi

Raji

2020

IET Information Security

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“…De Salve et al in the paper Predicting the availability of users devices in decentralized online social networks investigate whether availability patterns of users in online social networks (OSNs) can be predicted. The paper proposes a flexible linear predictor whose goal is to estimate the future availability of a user (ie, online or offline) by taking into account only the availability history of that user.…”

mentioning

confidence: 99%

Large Scale Cooperative Virtual Environments

Ricci

Iosup

Prodan

2018

Concurrency and Computation

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The recent advances in networking have determined an increasing use of information technology to support interactive networked cooperative applications. Several novel applications have emerged in this area: social networks, distributed virtual environments (DVEs), collaborative learning systems, large-scale crowd-based applications, and mobile collaborative platforms. This kind of applications can be generally referred as Large ScaleCooperative Virtual Environments. The definition of these applications requires affording several challenges such as the design of user interfaces, coordination protocols, mobility models, and proper middleware and architectures supporting distributed cooperation. Collaborative applications may greatly benefit from the support of different kinds of platforms, both cloud and peer to peer and also platforms recently proposed for the Internet of things (IoT) like fog computing. Integration of different platforms, for instance, mobile and cloud environments, is currently a challenge.Furthermore, the analysis and validation of the huge amount of content generated by these applications asks for proper data analysis and processing techniques. The special issue's aim is to investigate open challenges for such applications, related to both the applications' design and the definition of proper architectures. Some important challenges are, for instance, collaborative protocols design, large-scale processing of user information, privacy and security issues, state consistency/persistence, and efficient support definition.This special includes six high-quality papers, five of them are extended versions of the papers accepted and presented at the 4th Workshop on Large Scale Distributed Virtual Environments on Clouds and P2P held in conjunction with Euro-Par 2016 in Grenoble, France, in August 2016. In the following, we give a brief description of each paper.De Salve et al in the paper Predicting the availability of users devices in decentralized online social networks 1 investigate whether availability patterns of users in online social networks (OSNs) can be predicted. The paper proposes a flexible linear predictor whose goal is to estimate the future availability of a user (ie, online or offline) by taking into account only the availability history of that user. The information returned by the predictor is useful to support information allocation/diffusion in distributed OSNs (DOSNs). The predictor may be configured by setting a set of parameters like the size of the period of time in the future to predict and the size of the elapsed period of time exploited for the prediction. Furthermore, the paper proposes several selection strategies to choose users more likely to be online in a time interval. These strategies can be exploited to select the peers which host data replica in a DOSN.The paper Efficient and scalable execution of smart city parallel applications 2 presents an analysis and prediction of internet traffic generated by vehicle and pedestrian devices moving across a "smart avenue". The...

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Predicting the availability of users' devices in decentralized online social networks

Cited by 6 publications

References 26 publications

Predicting Influential Users in Online Social Network Groups

Predicting Influential Users in Online Social Network Groups

Data availability improvement in peer‐to‐peer online social networks

Large Scale Cooperative Virtual Environments

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