• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.421-433
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• 2015

Recently, there have been fast-growing social network services based on the Internet environment and web technology development, the prevalence of smartphones, etc. Social networks also allow the users to convey the information and news so that they have a great influence on the public opinion formed by social interaction among users as well as the spread of information. On the other hand, these social networks also serve as perfect environments for rampant malware. Malware is rapidly being spread because relationships are formed on trust among the users. In this paper, an effective patch strategy is proposed to deal with malicious worms based on social networks. A graph is formed to analyze the structure of a social network, and subgroups are formed in the graph for the distributed patch strategy. The weighted directions and activities between the nodes are taken into account to select reliable key nodes from the generated subgroups, and the Incremental PageRanking algorithm reflecting dynamic social network features (addition/deletion of users and links) is used for deriving the high influential key nodes. With the patch based on the derived key nodes, the proposed method can prevent worms from spreading over social networks.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.455-468
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• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.

• Journal of Distribution Science
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• v.14 no.9
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• pp.31-45
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• 2016

Purpose - This study develops a simulation model that looks at the dynamics between social capital and business performance in strategic networks to understand their behaviors in relation to each other, and to suggest dynamic relationship strategies. Research design, data, and methodology - Based on existing literature, this study identifies the complex causal loop diagram on social capital and business performance in strategic networks, and converts them into a simulation model for observing how the changes in business environment and relationship dependency affect social capital and business performance. Results - The simulation results showed that, first, the formation in social capital and business performance of networks with low relationship dependency was less affected by the changes in business environment. Second, the formation in social capital and business performance of networks with high relationship dependency was negatively impacted by the changes in business environment. In other words, higher relationship dependency strengthened the impact of changes in business environment on business performance. Conclusions - Thus, this study confirmed that in strategic networks, the changes in business environment and the degree of relationship dependency dynamically affect business performance, and that relationship dependency mediates the degree in which changes in the business environment affect business performance. The results of the simulations were further verified through actual business cases.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.25-38
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• 2021

Recently the need for network surveillance to detect abnormal behavior within dynamic social networks has increased. We consider a dynamic version of the degree corrected stochastic block model (DCSBM) to simulate dynamic social networks and to monitor for a significant structural change in these networks. To apply a control charting procedure to network surveillance, in-control model parameters must be estimated from the Phase I data, that is from historical data. In network surveillance, however, there are many situations where sufficient relevant historical data are unavailable. In this paper we propose a self-starting Shewhart control charting procedure for detecting change in the dynamic networks. This procedure can be a very useful option when we have only a few initial samples for parameter estimation. Simulation results show that the proposed procedure has good in-control performance even when the number of initial samples is very small.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.41-50
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• 2013

Social network analysis use the n nodes with l connections. About dozens or hundreds number of nodes are reasonable for social network analysis to the entire data. Beyond such number of nodes it will be difficult to analyze entire data. Therefore, it is necessary to separate the whole social networks, a method that can be used at this time is Clustering. You will be able to easily perform the analysis of the features of social networks and the relationships between nodes, if sub-group consists of all the nodes by Clustering. Clustering algorithm needs the interaction with the user and computer because it is need to pre-set the number of sub-groups. Sub-groups generated like this can not be guaranteed optimal results. In this paper, we propose dynamic sub-groups creating method using the external community association. We compared with previous studies by the number of sub-groups and sub-groups purity standards. Experimental results show the excellence of the proposed method.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.504-513
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• 2013

Communication in delay tolerant networks (DTNs) relies on message transport by mobile nodes, and a correct understanding of the node mobility characteristics is therefore crucial to the design of an efficient DTN routing protocol. However, previous work has mainly focused on uncovering all behaviors of node movement, which is not conducive to accurately detecting the specific movement characteristics of a different node. In this paper, we seek to address this problem based on a consideration of social relationships. We first consider social ties from both static and dynamic perspectives. For a static perspective, in addition to certain accidental events, social relations are considered for a long time granularity and tend to be stable over time. For a dynamic perspective, social relations are analyzed in a relatively short time granularity and are likely to change over time. Based on these perspectives, we adopted different efficient approaches to dividing node pairs into two classes, i.e., familiar and unfamiliar pairs. A threshold approach is used for static social ties whereas a density-based aggregation method is used for dynamic social relationships. Extensive experimental results show that both familiar and unfamiliar node pairs have the same inter-contact time distribution, which closely follows a power-law decay up to a certain point, beyond which it begins to exponentially decay. The results also demonstrate that the inter-contact time distribution of familiar pairs decays faster than that of unfamiliar pairs, whether from a static or dynamic perspective. In addition, we also analyze the reason for the difference between the inter-contact time distributions of both unfamiliar and familiar pairs.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.131-140
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• 2009

Recently, the information visualization research community has given significant attention to graph visualization, especially visualization of social networks. However, visualization of information flows in a very large social network has not been studied in depth. However, information flows are tightly related to the structure of social networks and it shows dynamic behavior of interactions between members of social networks. Thus, we can get much useful information about social networks from information flows. In this paper, we present our research result that enables users to navigate a very large social network in Google Maps' method and to take a look at information flows on the network. To this end, we devise three techniques; (i) mapping a very large social network to a 2-dimensional graph layout, (ii) exploring the graph to all directions with zooming it in/out, and (iii) building an efficient query processing framework. With these methods, we can visualize very large social networks and information flows in a limited display area with a limited computing resources.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.110-117
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• 2015

In mobile social network, it is a critical challenge to select an optimal set of devices to supply high quality service constantly under dynamic network topology and the limit of device capacity in mobile ad-hoc network (MANET). In this paper, a multi-devices composition and maintenance problem is proposed with ubiquitous service model and network model. In addition, a multi-devices composition and maintenance approach with dynamic planning is proposed to deal with this problem, consisting of service discovery, service composition, service monitor and service recover. At last, the simulation is implemented with OPNET and MATLAB and the result shows this mechanism is better applied to support complex ubiquitous service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3568-3584
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• 2016

In mobile opportunistic networks (MONs), human-carried mobile devices such as PDAs and smartphones, with the capability of short range wireless communications, could form various intermittent contacts due to the mobility of humans, and then could use the contact opportunity to communicate with each other. The dynamic changes of the network topology are closely related to the human mobility patterns. In this paper, we propose a social motivation-aware mobility model for MONs, which explains the basic laws of human mobility from the psychological point of view. We analyze and model social motivations of human mobility mainly in terms of expectancy value theory and affiliation motivation. Furthermore, we introduce a new concept of geographic functional cells, which not only incorporates the influence of geographical constraints on human mobility but also simplifies the complicated configuration of simulation areas. Lastly, we validate our model by simulating three real scenarios and comparing it with reality traces and other synthetic traces. The simulation results show that our model has a better match in the performance evaluation when applying social-based forwarding protocols like BUBBULE.