• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1454-1466
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• 2016

This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.71-77
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• 2015

This paper implements an estimated closeness centrality ranking algorithm in large-scale workflow-supported social networks and performance analyzes of the algorithm. Existing algorithm has a time complexity problem which is increasing performance time by network size. This problem also causes ranking process in large-scale workflow-supported social networks. To solve such problems, this paper conducts comparison analysis on the existing algorithm and estimated results by applying estimated-driven RankCCWSSN(Rank Closeness Centrality Workflow-supported Social Network). The RankCCWSSN algorithm proved its time-efficiency in a procedure about 50% decrease.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.47-53
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• 2016

A type of workflow affiliation network is one of the specialized social network types, which represents the associative relation between actors and activities. There are many methods on a workflow affiliation network measuring centralities such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In particular, we are interested in the closeness centrality measurements on a workflow affiliation network discovered from enterprise workflow models, and we know that the time complexity problem is raised according to increasing the size of the workflow affiliation network. This paper proposes an estimated ranking algorithm and analyzes the accuracy and average computation time of the proposed algorithm. As a result, we show that the accuracy improves 47.5%, 29.44% in the sizes of network and the rates of samples, respectively. Also the estimated ranking algorithm's average computation time improves more than 82.40%, comparison with the original algorithm, when the network size is 2400, sampling rate is 30%.

• Journal of Distribution Science
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• v.17 no.3
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• pp.85-95
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• 2019

Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

• Journal of Family Resource Management and Policy Review
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• v.17 no.4
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• pp.85-100
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• 2013

The purposes of this study are to explore the effect of the network characteristics of Healthy Family Support Center on its performance, and also to investigate the mediating effect of the organizational properties on the performance. We used the data from 148 healthy family support centers in National Survey in Korea. The analytic sample for this study consists of 102 responses.(response rate=68.9%) Multivariate regression model estimated the effects of the network's structural, interactive and functional characteristics and the interaction between the network's characteristics and organizational properties on the performance The findings of this study demonstrate that healthy family support centers with higher closeness centrality and with better functional characteristics reported more performances. Moreover, Centers that are more independent in organizational properties showed higher performances. However, the findings did not show that the interaction between the network's characteristics and organizational properties mediates on the performance.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.529-538
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• 2018

The port industry is an important national industry which significantly affects Korea's imports and exports which are centered on economic structure. For instance, the Port of Busan, which handles 75% of domestic container freight volume, is expected to become increasingly critical for container liner routes. For this reason, there have been continued efforts to expand freight service to attract international freight volume. This study analyzes the structural characteristics of the port network connected to the Port of Busan by analyzing the pattern of the container liner route from 2012 to 2016 by using social network analysis. According to the Port of Busan's liner route network, the port with the highest degree of centrality, closeness centrality, and betweenness centrality was found to be the Port of Singapore. The comparison of Busan's annual container handling rank by countries and the port center network analysis of Port of Busan rank was found to be different. As a result, it was established that China's East Port, which occupies a high percentage of the volume of cargo handled by Port of Busan, is not a hub port of Busan when viewed on the Busan's container terminal liner network. In addition, even if the number of Port of Busan container liner service increases, it is estimated that the vessels to be added to the fleet will be limited to small to medium sized, or that Busan port has characteristic of a feeder port for the Port of Singapore, according to the network.