An Improved Realistic Group Mobility Model for MANET Based On Unified Relationship Matrix

Gunasekaran, S.; Nagarajan, N.

doi:10.1109/iadcc.2009.4809198

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…The intersection of the two is represented by 0 or 1, where 0 means no intersection and 1 means intersection. Based on this, we can identify which accounts generate connections between them [ 70 , 71 , 72 ]. The matrix data were also imported into UCINET 6 social network analysis software for the next analysis.…”

Section: Methodsmentioning

confidence: 99%

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo

Deng

Yang

2021

IJERPH

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The COVID-19 pandemic has created a global health crisis that has affected economies and societies worldwide. During these times of uncertainty and crisis, people have turned to social media platforms as communication tools and primary information sources. Online discourse is conducted under the influence of many different factors, such as background, culture, politics, etc. However, parallel comparative research studies conducted in different countries to identify similarities and differences in online discourse are still scarce. In this study, we combine the crisis lifecycle and opinion leader concepts and use data mining and a set of predefined search terms (coronavirus and COVID-19) to investigate discourse on Twitter (101,271 tweets) and Sina Weibo (92,037 posts). Then, we use a topic modeling technique, Latent Dirichlet Allocation (LDA), to identify the most common issues posted by users and temporal analysis to research the issue’s trend. Social Network Analysis (SNA) allows us to discover the opinion leader on the two different platforms. Finally, we find that online discourse reflects the crisis lifecycle according to the stage of COVID-19 in China and the US. Regarding the status of the COVID-19 pandemic, users of Twitter tend to pay more attention to the economic situation while users of Weibo pay more attention to public health. The issues focused on in online discourse have a strong relationship with the development of the crisis in different countries. Additionally, on the Twitter platform many political actors act as opinion leaders, while on the Weibo platform official media and government accounts control the release of information.

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Section: Methodsmentioning

confidence: 99%

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo

Deng

Yang

2021

IJERPH

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“…Both [16] and [17] make use of real world traces to build their models for generating synthetic traces. In [18], a unified relationship matrix is used to describe social strengths between people within the same community or in different communities. This in turn determines the colocation of people.…”

Section: Previous Workmentioning

confidence: 99%

Grammatical Inference for Modeling Mobility Patterns in Networks

Geyik

Bulut

Szymanski

2013

IEEE Trans. on Mobile Comput.

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Modeling of the mobility patterns arising in computer networks requires a compact and faithful representation of the mobility data collected from observations and measurements of the relevant network applications. This data can range from the information on the mobility of the agents that are being monitored by a wireless network to mobility information of nodes in mobile network applications. In this paper, we examine the use of probabilistic context free grammars as the modeling framework for such data. We present a fast algorithm for deriving a concise probabilistic context free grammar from the given training data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We describe the application of this algorithm in two mobility modeling domains: (i) recognizing mobility patterns of monitored agents in different event datasets collected by sensor networks, and (ii) modeling and generating node movements in mobile networks. We also discuss the model's performance in simulations utilizing both synthetic and real world mobility traces.

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“…In other words, nodes move towards other nodes or other communities based upon probabilities and taking into account the strength social association between nodes. There are some works that propose improvements to CMM ( [8], [5]), however, there are key aspects that these models cannot provide. For instance, they do not take into consideration pauses or the needs to change their direction on the fly.…”

Section: A Review Of Human Mobilitymentioning

confidence: 99%

Improving mobile networks based on social mobility modeling

Ribeiro

Sofia

Zúquete

2011

2011 19th IEEE International Conference on Network Protocols

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Wireless networks growing popularity coupled with a wide availability of wireless-enabled personal devices is today the basis for user-centric Internet architectures to evolve. Central to this new paradigm of user-centricity is the fact that today the Internet end-user exhibits a highly nomadic behavior, where most of the portable devices are carried by humans. The thesis proposed relates to the recent trend of social mobility modeling as a way to improve mobile network operation. The work is focused on the analysis of social mobility models and how to propose new models having in mind an overall network optimization due to the possibility to predict adequately node movement on mobile networks.Index Terms-wireless networks, social mobility models, human movement.

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An Improved Realistic Group Mobility Model for MANET Based On Unified Relationship Matrix

Cited by 7 publications

References 7 publications

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo

Grammatical Inference for Modeling Mobility Patterns in Networks

Improving mobile networks based on social mobility modeling

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