Mining community and inferring friendship in mobile social networks

Xu, Ke; Zou, Keju; Huang, Yan; Yu, Xiaoyang; Zhang, Xinfang

doi:10.1016/j.neucom.2015.09.070

Cited by 45 publications

(13 citation statements)

References 34 publications

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“…However, the two are not equivalent to a limited area or a non-closed spherical area. The conditions of no isolated nodes in the network are weak, and the conditions under which the network is connected are strong [16]. As the density of nodes increases, the network first reaches the state of no isolated nodes, and further increases the node density to reach the fully connected state [14].…”

Section: Design Of Node Density Control Learning Algorithmmentioning

confidence: 99%

A Node Density Control Learning Method for the Internet of Things

Lou

Srivastava

Liu

2019

Sensors

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When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.

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Section: Design Of Node Density Control Learning Algorithmmentioning

confidence: 99%

A Node Density Control Learning Method for the Internet of Things

Lou

Srivastava

Liu

2019

Sensors

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“…They apply their approach in the Twitter reciprocal reply networks for the experimentation. Furthermore, Xu et al [47] propose an algorithm for predicting friendship relationships in mobile social networks. Their algorithm is mainly based on the locations and times where the users check in.…”

Section: Accepted Manuscript 24 Social Network Algomentioning

confidence: 99%

A hybrid approach with agent-based simulation and clustering for sociograms

García‐Magariño

Medrano

Lombas

et al. 2016

Information Sciences

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In the last years, some features of sociograms have proven to be strongly related to the performance of groups. However, the prediction of sociograms according to the features of individuals is still an open issue. In particular, the current approach presents a hybrid approach between agent-based simulation and clustering for simulating sociograms according to the psychological features of their members. This approach performs the clustering extracting certain types of individuals regarding their psychological characteristics, from training data. New people can then be associated to one of the types in order to run a sociogram simulation. This approach has been implemented with the tool called CLUS-SOCI (an agent-based and CLUStering tool for simulating SOCIograms). The current approach has been experienced with real data from four different secondary schools, with 38 real sociograms involving 714 students. Two thirds of these data were used for training the tool, while the remaining third was used for validating it. In the validation data, the resulting simulated sociograms were similar to the real ones in terms of cohesion, coherence of reciprocal relations and intensity, according to the binomial test with the correction of Bonferroni.

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“…In light of the increasingly popular public mobile networks and mobile terminal devices, the social networking that only used to be attached to computer terminals has begun to apply more efficient and convenient mobile devices in providing the MSNs participants with mobile Internet access services anytime and anywhere (Ou, Pan, & Li, 2012;Peng, Yang, Cao et al, 2017). In particular, free public WiFi hotspots are widely used in people's daily life and leisure places, further helping mobile users to participate in various online social activities without need for too much money or even free of charge (Xu, Zou, Huang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for College English Teaching Based on Mobile Social Networks and Student Psychology

2020

RACP

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In recent years, mobile social networks (MSNs) have been adopted for teaching practices in colleges, thanks to their convenience and stability. This paper firstly analyses the feasibility of applying the MSNs in college English teaching, and then discusses current psychological status of college students. On this basis, a MSNs-based framework was set up for English teaching in colleges. Next, a contrastive experiment was carried out on two classes of English majors. The two classes respectively received MSNs-based English teaching and traditional mode of English teaching. The psychology of all the subjects was examined in different aspects. The results show that the use of the MSNs greatly promoted the psychology and performance of English learners. This research further promotes the integration of cutting-edge technologies in college English education.

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Mining community and inferring friendship in mobile social networks

Cited by 45 publications

References 34 publications

A Node Density Control Learning Method for the Internet of Things

A Node Density Control Learning Method for the Internet of Things

A hybrid approach with agent-based simulation and clustering for sociograms

A Novel Framework for College English Teaching Based on Mobile Social Networks and Student Psychology

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