• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.53-62
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• 2023

Effects-Based Operations (EBO) refers to a process for achieving strategic goals by focusing on effects rather than attrition-based destruction. For a successful implementation of EBO, identifying key nodes in an adversary network is crucial in the process of EBO. In this study, we suggest a network-based approach that combines network centrality and optimization to select the most influential nodes. First, we analyze the adversary's network structure to identify the node influence using degree and betweenness centrality. Degree centrality refers to the extent of direct links of a node to other nodes, and betweenness centrality refers to the extent to which a node lies between the paths connecting other nodes of a network together. Based on the centrality results, we then suggest an optimization model in which we minimize the sum of the main effects of the adversary by identifying the most influential nodes under the dynamic nature of the adversary network structure. Our results show that key node identification based on our optimization model outperforms simple centrality-based node identification in terms of decreasing the entire network value. We expect that these results can provide insight not only to military field for selecting key targets, but also to other multidisciplinary areas in identifying key nodes when they are interacting to each other in a network.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.25-31
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• 2016

There are many methods to describe the importance of a node, centrality, in a graph. In this paper, we focus on the eigenvector centrality. In this paper, an analytical method to estimate the difference of centrality with an additional edge in a graph is proposed. In order to validate the analytical method to estimate the centrality, two problems, to decide an additional edge that maximizes the difference of all centralities of all nodes in the graph and to decide an additional edge that maximizes the centrality of a specific node, are solved using three kinds of random graphs and the results of the estimated edge and observed edge are compared. Though the estimated centrality difference is slightly different from the observed real centrality in some cases, it is shown that the proposed method is effective to estimate the centrality difference with a short running time.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.121-126
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• 2022

The shortest path betweenness value of a node quantifies the amount of information passing through the node when all the pairs of nodes in the network exchange information in full capacity measured by the number of the shortest paths between the pairs assuming that the information travels in the shortest paths. It is calculated as the cumulative of the fractions of the number of shortest paths between the node pairs over how many of them actually pass through the node of interest. It's possible for a node to have zero or underrated betweenness value while sitting just next to the giant flow of information. These nodes may have a significant influence on the network when the normal flow of information is disrupted. We propose a betweenness centrality measure called collective betweenness that takes into account the surroundings of a node. We will compare our measure with other centrality metrics and show some applications of it.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.39-47
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• 2015

The centrality is calculated to describe the importance of a node in a graph and ranking is given according to the centrality for each node. There are many centrality measures and we use degree centrality, closeness centrality, eigenvector centrality, and betweenness centrality. In this paper, we analyze the effect of an additional edge of a graph on centrality and ranking through experimental computations. It is found that the effect of an additional edge on centrality and ranking of the nodes in the graph is different according to the graph structure using PCA. The results can be used for define the graph characteristics.

• Journal of the Korean Society for information Management
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• v.30 no.4
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• pp.241-264
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• 2013

This study compared and analyzed weighted network centrality measures supported by Opsahl's tnet and Lee's WNET, which are free softwares for weighted network analysis. Three node centrality measures including weighted degree, weighted closeness, and weighted betweenness are supported by tnet, and four node centrality measures including nearest neighbor centrality, mean association, mean profile association, triangle betweenness centrality are supported by WNET. An experimental analysis carried out on artificial network data showed tnet's high sensitiveness on linear transformations of link weights, however, WNET's centrality measures were insensitive to linear transformations. Seven centrality measures from both tools, tnet and WNET, were calculated on six real network datasets. The results showed the characteristics of weighted network centrality measures of tnet and WNET, and the relationships between them were also discussed.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.37-44
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• 2015

In traditional social network analysis, the betweenness centrality measure has been heavily used to identify the relative importance of nodes. Since the time complexity to calculate the betweenness centrality is very high, however, it is difficult to get it of each node in large-scale social network where there are so many nodes and edges. In our past study, we defined a new type of network, called the expanded ego network, which is built only with each node's local information, i.e., neighbor information of the node's neighbor nodes, and also defined a new measure, called the expanded ego betweenness centrality. In this paper, We propose algorithm that quickly computes expanded ego betweenness centrality by exploiting structural properties of expanded ego network. Through the experiment with virtual network used Barab$\acute{a}$si-Albert network model to represent the generic social network and facebook network to represent actual social network, We show that the node's importance rank based on the expanded ego betweenness centrality has high similarity with that the node's importance rank based on the existing betweenness centrality. We also show that the proposed algorithm computes the expanded ego betweenness centrality quickly than existing algorithm.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.47-54
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• 2022

We analyze and compare the structure of the networks of Netflix, Disney+, and OCN, which are forerunners in OTT market, on Twitter. This study employs NodeXL pro as a visualization software package for social network analysis. As a result of the comparison with values of Vertices, Connected Components, Average Geodesic Distance, Average Betweenness Centrality, and Average Closeness Centrality. Netflix has comparative advantages at Vertices, Connected Components, and Average Closeness Centrality, OCN at Average Geodesic Distance, and Disney+ at Average Betweenness Centrality. Netflix has a more appropriate social network for influencer marketing than Disney+ and OCN. Based on the analysis results, the purpose of this study is to explain the structural differences in the social networks of Netflix, Disney+, and OCN in terms of influencer marketing.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.5
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• pp.209-216
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• 2013

In traditional social network analysis, the betweenness centrality measure has been heavily used to identify the relative importance of nodes in terms of message delivery. Since the time complexity to calculate the betweenness centrality is very high, however, it is difficult to get it of each node in large-scale social network where there are so many nodes and edges. In this paper, we define a new type of network, called the expanded ego network, which is built only with each node's local information, i.e., neighbor information of the node's neighbor nodes, and also define a new measure, called the expended ego betweenness centrality. Through the intensive experiment with Barab$\acute{a}$si-Albert network model to generate the scale-free networks which most social networks have as their embedded feature, we also show that the nodes' importance rank based on the expanded ego betweenness centrality has high similarity with that based on the traditional betweenness centrality.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.81-94
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• 2023

The study aims to compare and analyze the social network structures of Qatar Airways,s Singapore Airlines, Emirates Airlines, and ANA Airlines, recording the top 1 to 4, and Korean Air in ninth by Skytrax's airline evaluations in 2022. This study uses NodeXL, a social network analysis program, to analyze the social networks of 5 airlines, Vertex, Unique Edges, Single-Vertex Connected Components, Maximum Geodesic Distance, Average Geodesic Distance, Average Degree Centrality, Average Closeness Centrality, and Average Betweenness Centrality as indicators to compare the differences in these social networks of the airlines. As a result, Singapore's social network has a better network structure than the other airlines' social networks in terms of sharing information and transmitting resources. In addition, Qatar Airways and Singapore Airlines are superior to the other airlines in playing roles and powers of influencers who affect the flow of information and resources and the interaction within the airline's social network. The study suggests some implications to enhance the usefulness of social networks for marketing.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.107-121
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• 2015

Diffusion of innovation is the process in which an innovation is communicated through certain channels over time among the members of a social system. The literatures have emphasized the importance of interpersonal network influences on individuals in convincing them to adopt innovations and thereby promoting its diffusion. In particular, the behavior of opinion leaders who lead in influencing others' opinion is important in determining the rate of adoption of innovation in a system. Centrality has been recognized as a good indicator that quantifies a node's influences on others in a given network. However, recent studies have questioned its relevance on various different types of diffusion processes. In this regard, this study aims at examining the effect of a node exhibiting high centrality on expediting diffusion of innovations. In particular, we considered the situation where two innovations compete with each other to be adopted by potential adopters who are personally connected with each other. In order to analyze this competitive diffusion process, we developed a simulation model and conducted regression analyses on the outcomes of the simulations performed. The results suggest that the effect of a node with high centrality can be substantially reduced depending upon the type of a network structure or the adoption thresholds of potential adopters in a network.