• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.7-24
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• 2009

Since the Web is dynamic, it is necessary to analyze scholarly Web space with both quantitative and qualitative methods for better understanding of communication characteristics. In this study, we analyzed contents of pages and links to ascertain the characteristics of Korean scholarly Web space in terms of network structure and communication behavior. The result shows that the structure of the original network with all the external links remained is not much different from that of the network with activated external links only. However, the purposes of linking vary among scholarly institutions. The centrality measures correlate more strongly with the clustering coefficient than with the constraint index implying the similar explanatory power of the two types of structural indices.

• Communications of Mathematical Education
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• v.29 no.3
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• pp.465-489
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• 2015

The purpose of this study is to suggest implications for the development and revision of future teaching materials for mathematically gifted students by using network text analysis of secondary mathematics materials. Subjects of the analysis were learning goals of 110 teaching materials in a gifted education center in science attached to a university from 2002 to 2014. In analysing the frequency of the texts that appeared in the learning goals, key words were selected. A co-occurrence matrix of the key words was established, and a basic information of network, centrality, centralization, component, and k-core were deducted. For the analysis, KrKwic, KrTitle, and NetMiner4.0 programs were used, respectively. The results of this study were as follows. First, there was a pivot of the network formed with core hubs including 'diversity', 'understanding' 'concept' 'method', 'application', 'connection' 'problem solving', 'basic', 'real life', and 'thinking ability' in the whole network from 2002 to 2014. In addition, knowledge aspects were well reflected in teaching materials based on the centralization analysis. Second, network text analysis based on the three periods of the Mater Plan for the promotion of gifted education was conducted. As a result, a network was built up with 'understanding', and there were strong ties among 'question', 'answer', and 'problem solving' regardless of the periods. On the contrary, the centrality analysis showed that 'communication', 'discovery', and 'proof' only appeared in the first, second, and third period of Master Plan, respectively. Therefore, the results of this study suggest that affective aspects and activities with high cognitive process should be accompanied, and learning goals' mannerism and ahistoricism be prevented in developing and revising teaching materials.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.5
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• pp.53-61
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• 2004

This paper proposes two types of sub-network based on dynamic restoration schemes to improve survivability of GMPLS networks and analyzes characteristics of these two schemes. The first proposed scheme divides with a whole GMPLS network into several sub-networks, applies a mixture of both restoration and protection methods according to fault location. The other scheme divides a whole GMPLS network into primary and secondary sub-networks, applies a restoration method in each sub-network according to fault location. In our simulation, we evaluate the performances of network resource utilization, restoration success rate, and mean restoration time and conduct its comparative analysis with conventional schemes. The simulation results show that the efficiency of network resource utilization in the proposed schemes is increased as compared with conventional restoration schemes (l＋l, 1:1, 1:N) in case of single-failed link. By contrast, we found that the performances of restoration success rate and mean restoration time in case of multi-failed link is lower than conventional restoration schemes. However, the probability that multi-failed link is occurred is very low so that the problem in practical GMPLS network is negligible.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.616-625
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• 2015

The purpose of the study is to structure the friendship network by the social network analysis and investigate the effects of social network centrality and learners' performances in college students. Both the in-degree centrality of 1st grade class study-network(t=2.722, P<.005) and the in-degree centrality of and $2^{nd}$ grade class study-network(t=2.708, P<.005)are predicted the individual student's learning performances. But there is no correlation between the in-degree centrality of $1^{st}$ and $2^{nd}$ grade class entertainment-network and the individual student's learning performances. Results of the study suggested the significant effect of social network analysis measures on learners' performance in the friendship networks. Based on the results, implication to the teaching strategy and future research direction were discussed.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.259-271
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• 2008

We propose a protein function finding algorithm that is able to predict specific molecular function for unannotated proteins through domain analysis from protein-protein network. To do this, we first construct protein-protein interaction(PPI) network in Saccharomyces cerevisiae from MIPS databases. The PPI network(proteins; 3,637, interactions; 10,391) shows the characteristics of a scale-free network and a hierarchical network that proteins with a number of interactions occur in small and the inherent modularity of protein clusters. Protein-protein interaction databases obtained from a Y2H(Yeast Two Hybrid) screen or a composite data set include random false positives. To filter the database, we reconstruct the PPI networks based on the cellular localization. And then we analyze Hub proteins and the network structure in the reconstructed network and define structural modules from the network. We analyze protein domains from the structural modules and derive functional modules from them. From the derived functional modules with high certainty, we find tentative functions for unannotated proteins.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.429-436
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• 2002

In order to apply the artificial neural network to the practical application, it is needed to implement it with the hardware system. It is most promising to make it with the hybrid VLSI among various possible technologies. When we Implement a trained network into the hybrid neuro-chips, it is to be performed the process of the quantization on its neuron outputs and its weights. Unfortunately this process cause the network's outputs to be distorted from the original trained outputs. In this paper we analysed in detail the statistical characteristics of the distortion. The analysis implies that the network is to be trained using the normalized input patterns and finally into the solution with the small weights to reduce the distortion of the network's outputs. We performed the experiment on an application in the time series prediction area to investigate the effectiveness of the results of the analysis. The experiment showed that the network by our method has more smaller distortion compared with the regular network.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1589-1600
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• 1997

The construction of optical access network costs upper 40% in total installation cost of total optical network. Optimization of access network therefore is core in optical network. Advanced countries include EU, Japan and USA already have researched access network. This paper presents analysis of three broad-band fiber-optics subscriber loop architectures(HFC, ATM-PON, Super PON). The analyses focus on the specific demonstrated architectures and use component cost projections to estimate future network costs on a per-subscriber basis. We use TITAN(Tool for Introduction Scenarios and Techno-Economic Evaluation of The Access Network) model. We find that ATM-PON can deliver voice and ISDN data at installed first costs than the other architectures. This is due to the sharing bandwidth among a cluster of subscribers within Curb. This work intends to support establishing guidelines for strategic decisions regarding the development of the access network alternatives of different operators.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.656-661
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• 2008

There are many sociological methodology for the study on cyber community. Especially for the online social network which is constructed by the notion of the social network, the social network analysis can be a superior methodology for the understanding of the general phenomenon and the solutions for the problematic issues. As pointed by Manuel Castells, the problem of the unification and the fragmentation, which is came after the participation and the openness, is prevailed in cyber community by its characteristics. However, computational properties that exceed the limitation of the time and space can bestow other alternatives. The Open API in the social network services that is more popular in these days is the one of those alternatives. Therefore, the purpose of this study is to investigate the problematic phenomenon of online social network and to search the solutions for those problems with the acceptance of Open API technology.