• Honam Mathematical Journal
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• v.2 no.1
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• pp.9-12
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• 1980

Kasahara는 임의(任意)의 집합(集合)에 Closeness Structure를 도입(導入)하여 Convergence Structure와의 관계(關係)를 밝혔는데 본(本) 논문(論文)에서는 Closeness 공간(空間) (X, ${\Gamma}$)의 부분집합(部分集合) Y가 X 상(上)의 Closeness Structure ${\Gamma}$에 대(對)한 상대(相對) Closeness Structure를 갖기 위(爲)한 조건(條件) 및 Closeness 부분공간(部分空間)과 Convergence 부분공간(部分空間)과의 관계(關係)를 고찰(考察)하였다.

• Journal of Distribution Science
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• v.18 no.3
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• pp.25-36
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• 2020

Purpose: Based on previous researches on social factors of digital item purchase in digital contents distribution platforms such as SNS, we aim to develop the integrated model that accounts for the dynamic and interactive relationship between social structure indicators and digital item purchase. Research design, data and methodology: A PVAR model was used to capture endogenous and dynamic relationships between digital item purchase and network indicators. Results: We find that there exist considerable endogenous and dynamic relationships between digital item purchase and network structure variables. Not only lagged in-degree and out-degree but also in-closeness and out-closeness centrality have significant and positive impacts on digital item purchase. Lagged clustering has a significant and negative effect on digital item purchase. Lagged purchase has a significant and positive impact just on the present in-closeness and out-closeness centrality; but there is no significant effect of lagged purchase on the other two degree variables and clustering coefficient. We also find that both closeness centralities have much higher carryover effect on digital item purchase and that the elasticity of both closeness centralities on the purchase of digital items is even higher than that of other network structure variables. Conclusions: In-closeness and out-closeness are the most influential factors among social structure variables of this study on digital item purchase.

• Journal of Society of Preventive Korean Medicine
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• v.12 no.2
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• pp.85-100
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• 2008

The purpose of this study was to determine factors influencing family closeness in family with an elderly members. The study was conducted with 1,904 subjects during 3 months from 1st March to 30,May. 2006. The results were as follows. 1. Family closeness of subjects was significantly associated with age, sex, marital status, monthly allowance, education level, occupation, the number of family members living together, health state, stress, and emotional conflict with children. 2. Emotional conflict with children was significantly associated with age, sex, religion, the number of family members living together, occupation, health state, stress, family closeness. 3. Stress was significantly associated with age, sex, religion, the number of family members living together, occupation, stress, family closeness. Finaly, Family closeness in family with an elderly member was positively related to family type(living with a spouse), monthly allowance, occupation but negatively related to emotional conflict with children and stress levels. The government, social service units and experts need to pay more attention to factors influencing family closeness and devise effective policy and programs for healthier family relations.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.43-46
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• 2008

Centrality has been actively studied in network analysis field. In this paper we show a calculation method of closeness centrality for WSN. Since nodes in a sensor network are very scarce in energy and computation capability the calculation of the closeness is done in two tiers by dividing network into clusters. In first step closeness centrality for cluster heads is calculated. In the second step closeness of member nodes of the chosen cluster is computed in respect to that cluster itself.

• 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 muscle and joint health
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• v.22 no.1
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• pp.13-19
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• 2015

Purpose: The purpose of this study was to identify the relationships among network centrality, psychological well-being (PWBS), and intention to exercise maintenance in participants of an aquatic exercise program. Methods: Using a single-experimental design, 17 osteoarthritis patients participated in an aquatic exercise program. The questionnaire to connect the network of members was used to peer nomination by Moreno (1953). Data were analyzed with the UCINET using centrality (degree, closeness, betweenness) and SPSS using descriptive statistics, wilcoxon signed ranked test, and spearman's rho. Results: Closeness centrality, PWBS, and intention to exercise maintenance were significantly different between 4 weeks and 8 weeks. At 4 weeks, PWBS was positively correlated with closeness centrality. Intention to exercise maintenance was positively correlated with degree, closeness, and betweenness centrality. At 8 weeks, PWBS was positively correlated with closeness centrality. Intention to exercise maintenance was positively correlated with closeness centrality. Conclusion: The aquatic exercise program can be effective in increasing closeness centrality, psychological well-being, and intention to exercise maintenance. This was the first study attempted to analyze construction of member relationships in osteoarthritis patients participating an exercise program by using social network analysis.

• Korean Journal of Human Ecology
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• v.17 no.6
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• pp.1105-1114
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• 2008

The purpose of this study was to investigate the influence of income and emotional closeness with father/mother on middle and high school-adolescent's alienation The subjects were 327 middle and high school students who lived with two-parent in Keoungbok and whose household income was lower \4,000,000. The data were analyzed by t-test, ANOVA, Pearson's correlation, and stepwise multiple regression(using SPSS 12.1). Major findings were as follows: 1) Middle and high school students's alienation was difference. The level of the high school adolescent's alienation was higher than the middle school adolescent's alienation. 2) Middle and high school students's alienation was differed by level of income and emotional closeness with father/mother. The lower level of income and emotional closeness with father/mother, the higher level of adolescent's alienation. 3) Among the income, emotional closeness with father/mother, the income was more influential predictor on high school-adolescent's alienation. But the income was not a significant predictor of middle school-adolescent's alienation. emotional closeness with father was more influential predictor on middle school-adolescent's alienation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• Bulletin of the Korean Mathematical Society
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• v.58 no.2
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• pp.445-449
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• 2021

We define the relative self-closeness number N��(g) of a map g : X → Y, which is a generalization of the self-closeness number N��(X) of a connected CW complex X defined by Choi and Lee [1]. Then we compare N��(p) with N��(X) for a fibration $X{\rightarrow}E{\rightarrow\limits^p}Y$. Furthermore we obtain its rationalized result.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.77-85
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• 2013

This paper proposes a closeness centrality analysis algorithm for workflow-supported social networks that represent the collaborative relationships among the performers who are involved in a specific workflow model. The proposed algorithm uses the social network analysis techniques, particularly closeness centrality equations, to analyze the closeness centrality of the workflow-supported social network. Additionally, through an example we try to verify the accuracy and appropriateness of the proposed algorithm.