• Convergence Security Journal
• /
• v.23 no.2
• /
• pp.3-10
• /
• 2023

In the malware distribution network existing on the web, there is a central node that plays a key role in distributing malware. If you find and block this node, you can effectively block the propagation of malware. In this study, a centrality search method applied with risk analysis in a complex network is proposed, and a method for finding a core node in a malware distribution network is introduced through this approach. In addition, there is a big difference between a benign network and a malicious network in terms of in-degree and out-degree, and also in terms of network layout. Through these characteristics, we can discriminate between malicious and benign networks.

• Kyungpook Mathematical Journal
• /
• v.56 no.4
• /
• pp.1191-1205
• /
• 2016

The emergence of various Internet worms, including the stand-alone Code Red worm that caused a distributed denial of service (DDoS), has prompted many studies on their propagation speed to minimize potential damages. Many studies, however, assume the same probabilities for initially infected nodes to infect each node during their propagation, which do not reflect accurate Internet worm propagation modelling. Thus, this paper analyzes how Internet worm propagation speed varies according to the number of vulnerable hosts directly connected to infected hosts as well as the link costs between infected and vulnerable hosts. A mathematical model based on centrality theory is proposed to analyze and simulate the effects of degree centrality values and closeness centrality values representing the connectivity of nodes in a large-scale network environment on Internet worm propagation speed.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.3
• /
• pp.58-72
• /
• 2021

In July 2021, UNCTAD classified Korea as a developed country. After the Korean War in the 1950s, economic development was promoted despite difficult conditions, resulting in epoch-making national growth. However, in order to respond to the rapidly changing global economy, it is necessary to continuously study the domestic industrial ecosystem and prepare strategies for continuous change and growth. This study analyzed the industrial ecosystem of the automobile industry where it is possible to obtain transaction data between companies by applying complexity spatial network analysis. For data, 295 corporate data(node data) and 607 transaction data (link data) were used. As a result of checking the spatial distribution by geocoding the address of the company, the automobile industry-related companies were concentrated in the Seoul metropolitan area and the Southeastern(Dongnam) region. The node importance was measured through degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, and the network structure was confirmed by identifying density, distance, community detection, and assortativity and disassortivity. As a result, among the automakers, Hyundai Motor, Kia Motors, and GM Korea were included in the top 15 in 4 indicators of node centrality. In terms of company location, companies located in the Seoul metropolitan area were included in the top 15. In terms of company size, most of the large companies with more than 1,000 employees were included in the top 15 for degree centrality and betweenness centrality. Regarding closeness centrality and eigenvector centrality, most of the companies with 500 or less employees were included in the top 15, except for automakers. In the structure of the network, the density was 0.01390522 and the average distance was 3.422481. As a result of community detection using the fast greedy algorithm, 11 communities were finally derived.

• Journal of the Korean Society for Library and Information Science
• /
• v.40 no.3
• /
• pp.191-214
• /
• 2006

Recently, some bibliometric researchers tried to use the centrality analysis methods and the centrality measures which are standard tools in social network analysis. However the traditional centrality measures originated from social network analysis could not deal with weighted networks such as co-citation networks. In this study. new centrality measures for analyzing bibliometric networks with link weights are suggested and applied to three real network data, including an author co-citation network, a co-word network, and a website co-link network. The results of centrality analyses in these three cases can be regarded as Promising the usefulness of suggested centrality measures, especially in analyzing the Position and influence of each node in a bibliometric network.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.117-127
• /
• 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 the Korean Society for information Management
• /
• v.32 no.2
• /
• pp.7-23
• /
• 2015

While there are several measures for node centralities, such as betweenness and degree, few centrality measures for local centralities in weighted networks have been suggested. This study developed a generalized centrality measure for calculating local centralities in weighted networks. Neighbor centrality, which was suggested in this study, is the generalization of the degree centrality for binary networks and the nearest neighbor centrality for weighted networks with the parameter ${\alpha}$. The characteristics of suggested measure and the proper value of parameter ${\alpha}$ are investigated with 6 real network datasets and the results are reported.

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

• Journal of Intelligence and Information Systems
• /
• v.15 no.4
• /
• pp.159-182
• /
• 2009

We construct product networks from the retail transaction dataset of an off-line department store. In the product networks, nodes are products, and an edge connecting two products represents the existence of co-purchases by a customer. We measure the quantities frequently used for characterizing network structures, such as the degree centrality, the closeness centrality, the betweenness centrality and the centralization. Using the quantities, gender, age, seasonal, and regional differences of the product networks were analyzed and network characteristics of each product category containing each product node were derived. Lastly, we analyze the correlations among the three centrality quantities and draw a marketing strategy for the cross-selling.

• Convergence Security Journal
• /
• v.21 no.2
• /
• pp.47-56
• /
• 2021

Network topology visualization has been studied a lot since the past and developed with many tools. The network topology has strength in understanding the overall structure of a network physically and is useful for understanding data flow between nodes logically. Although there are existing tools, not many can be utilized efficiently while using the general network node data structure and express the topology similar to the actual network structure. In this paper, we propose an efficient method to visualize topology using only connection information of network nodes. The method finds the central node by using the centrality, the influence of nodes in the network, and visualizes the topology by dynamically segmenting all nodes and placing network nodes in 3D space using the weight of the child node. It is a straightforward method, yet it effectively visualizes in the form of an actual network structure.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.2
• /
• pp.95-104
• /
• 2018

Using network betweenness centrality we attempt to analyze the characteristics of Seoul metropolitan subway lines. Betweenness centrality highlights the importance of a node as a transfer point between any pairs of nodes. This 'transfer' characteristic is obviously of paramount importance in transit systems. For betweenness centrality, both traditional betweenness centrality measure and weighted betweenness centrality measure which uses monthly passenger flow amount between two stations are used. By comparing traditional and weighted betweenness centrality measures of lines characteristics of passenger flow can be identified. We also investigated factors which affect betweenness centrality. It is the number of passenger who get on or get off that significantly affects betweenness centrality measures. Through correlation analysis of the number of passenger and betweenness centrality, it is found out that Seoul metropolitan subway system is well designed in terms of regional distribution of population. Four measures are proposed which represent the passenger flow characteristics. It is shown they do not follow Power-law distribution, which means passenger flow is relatively evenly distributed among stations. It has been shown that the passenger flow characteristics of subway networks in other foreign cities such as Beijing, Boston and San Franciso do follow power-law distribution, that is, pretty much biased passenger flow traffic characteristics. In this study we have also tried to answer why passenger traffic flow of Seoul metropolitan subway network is more homogeneous compared to that of Beijing.