• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.9-11
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• 2022

Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the "information cocoon".

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.18-30
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• 2020

Recently, as graph-structured data is widely used in various fields such as social networks and semantic Webs, needs for an effective and efficient search on a large amount of graph data have been increasing. Previous keyword-based search methods often find results by considering only the relevance to a given query. However, they are likely to produce semantically similar results by selecting answers which have high query relevance but share the same content nodes. To improve the diversity of search results, we propose a top-k search method that finds a set of subtrees which are not only relevant but also diverse in terms of the content nodes by controlling their similarity. We define a criterion for a set of diverse answer trees and design two kinds of diversified top-k search algorithms which are based on incremental enumeration and A^⁎ heuristic search, respectively. We also suggest an improvement on the A^⁎ search algorithm to enhance its performance. We show by experiments using real data sets that the proposed heuristic search method can find relevant answers with diverse content nodes efficiently.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.29-41
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• 2021

Recently, there are increasing attempts to utilize deep learning methodology in the fashion industry. Accordingly, research dealing with various fashion-related problems have been proposed, and superior performances have been achieved. However, the studies for fashion style classification have not reflected the characteristics of the fashion style that one outfit can include multiple styles simultaneously. Therefore, we aim to solve the multi-label classification problem by utilizing the dependencies between the styles. A multi-label recognition model based on a graph convolution network is applied to detect and explore fashion styles' dependencies. Furthermore, we accelerate model training and improve the model's performance through transfer learning. The proposed model was verified by a dataset collected from social network services and outperformed baselines.

• The Journal of the Korea Contents Association
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• v.13 no.7
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• pp.16-23
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• 2013

This paper analyzes the artistic practical use of social network visualization through the aesthetic information interpretation. The first social network visualization has emerged as 'Sociogram' in the form of social network analysis(SNA). Since social network has complex, the analyzing technology of social network has emerged. Early social network visualization has a practical purpose to measure the social structure, but current social network visualization divided into various forms from artistic expressions through information. This paper divide into two categories based on the artistic application of social network visualization. First, this research focuses on the static graph based on analog. Second, this research analyze the category of interacted visualization to generate a real-time digital image. This paper presents the fusion of paradigm between engineering and art through this way.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.147-157
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• 2018

Recently, with the advancement of network technologies, and the activation of IoT and social network services, many graph stream data have been generated. As the relationship between objects in the graph streams changes dynamically, studies have been conducting to detect or analyze the change of the graph. In this paper, we propose a scheme to incrementally detect frequent patterns by using frequent patterns information detected in previous sliding windows. The proposed scheme calculates values that represent whether the frequent patterns detected in previous sliding windows will be frequent in how many future silding windows. By using the values, the proposed scheme reduces the overall amount of computation by performing only necessary calculations in the next sliding window. In addition, only the patterns that are connected between the patterns are recognized as one pattern, so that only the more significant patterns are detected. We conduct various performance evaluations in order to show the superiority of the proposed scheme. The proposed scheme is faster than existing similar scheme when the number of duplicated data is large.

• Design Convergence Study
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• v.14 no.4
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• pp.133-150
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• 2015

In this study, we proposed Interactive Visualization methods that can be analyzed relations between nations in various viewpoints such as period, issue using total 5211 of the UN General Assembly voting data.For this research, we devised a similarity matrix between nations and developed two visualization method based similarity matrix. The first one is Network Graph Visualization that can be showed relations between nations which participated in the vote of the UN General Assembly like Social Network Graph by year. and the second one is Proximity based Circular Visualization that can be analyzed relations between nations focus on one nation or Changes in voting patterns between nations according to time. This study have a great signification. that's because we proposed Proximity based Circular Visualization methods which merged Line and Circle Graph for network analysis that never tried from other cases of studies that utilize conventional voting data and made it. We also derived co-operatives of each visualization through conducting a comparative experiment for the two visualization. As a research result, we found that Proximity based Circular Visualization can be better analysis each node and Network Graph Visualization can be better analysis patterns for the nations.

• Bulletin of the Korean Mathematical Society
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• v.46 no.4
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• pp.789-802
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• 2009

We show that computation of a sequence of Richelot isogenies from specified supersingular Jacobians of genus-2 curves over $\mathbb{F}_p$ can be executed in $\mathbb{F}_{p2}$ or $\mathbb{F}_{p4}$ . Based on this, we describe a practical algorithm for computing a Richelot isogeny sequence.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.427-439
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• 2013

With the fast development of complex science, lots of social networks are studied. We know that the social network is widely applied in analyzing issues in human culture, economics and web sciences. Recently we witness that some researchers began to compare the social network constructed from fiction literatures(literature social network) and the real social network obtained from practice. But we point that previous approaches for literature social network have some drawbacks since they completely depend on the biographical dictionary constructed for a designated literature. So since the previous approach focus on the few important characters and peoples around them, we can not understand the global structure of all characters appeared in the literature at least once. We propose one method to extract all characters appeared in the literature and how to make the social network from that information. Also we newly propose K-critical network by applying frequency of the named characters and the strength of relationship among all textual characters. Our experiment shows that the K-critical measure could be one crucial quantitative measure to compute the relationship strength among characters appeared in the object literature.

• Journal of Families and Better Life
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• v.28 no.4
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• pp.15-28
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• 2010

The study investigated the repeated conflict of remarried family from family systems perspective, and used the verbatim of family members, who were participated in family therapy, as analysis data. the study used Miles and Hubernam's matrix, graph, and graphical network display to analyze the data. The results of study were as follows. After the couples remarried, a husband and two step-children, who were born in ex-wife, used dysfunctional communication method to keep the established family systems. Also, a wife used dysfunctional communication method to change the existing family systems. These dysfunctional communication method, which were used by family members, caused vicious cycle rather than resolving the family conflict. The differences of remarried husband first-married wife, and the differences of blooded and non-blooded relationships among family members had influenced on family conflict.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.699-700
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• 2013

This paper proposes a new visualization method based on cloud technique which uses internal relationship of user correlation and external relation of social network to visualize user relationship hierarchy. The proposed method use hadoop and hive for distribution storing and parallel processing which the result of calculation visualizes hierarchy graph using D3.