• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.147-169
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• 2024

In pace with the development of network technology at lightning speed, social networks have been extensively applied in our lives. However, as social networks retain a large number of users' sensitive information, the openness of this information makes social networks vulnerable to attacks by malicious attackers. To preserve the link privacy of individuals in social networks, an uncertain graph method based on node random response is devised, which satisfies differential privacy while maintaining expected data utility. In this method, to achieve privacy preserving, the random response is applied on nodes to achieve edge modification on an original graph and node differential privacy is introduced to inject uncertainty on the edges. Simultaneously, to keep data utility, a divide and conquer strategy is adopted to decompose the original graph into many sub-graphs and each sub-graph is dealt with separately. In particular, only some larger sub-graphs selected by the exponent mechanism are modified, which further reduces the perturbation to the original graph. The presented method is proven to satisfy differential privacy. The performances of experiments demonstrate that this uncertain graph method can effectively provide a strict privacy guarantee and maintain data utility.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.803-816
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• 2023

Explainable artificial intelligence is a method that explains how a complex model (e.g., a deep neural network) yields its output from a given input. Recently, graph-type data have been widely used in various fields, and diverse graph neural networks (GNNs) have been developed for graph-type data. However, methods to explain the behavior of GNNs have not been studied much, and only a limited understanding of GNNs is currently available. Therefore, in this paper, we propose an explanation method for node classification using graph convolutional networks (GCNs), which is a representative type of GNN. The proposed method finds out which features of each node have the greatest influence on the classification of that node using GCN. The proposed method identifies influential features by backtracking the layers of the GCN from the output layer to the input layer using the gradients. The experimental results on both synthetic and real datasets demonstrate that the proposed explanation method accurately identifies the features of each node that have the greatest influence on its classification.

• Journal of Korean Society of Rural Planning
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• v.6 no.2 s.12
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• pp.12-19
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• 2000

Irrigation pipeline networks consist mainly of buried pipes and are therefore relatively free from topographic constraints. Installation of irrigation pipeline systems is increasing since the systems have several advantages compared to open channel systems. To achieve economic design of pipeline networks, the layout should meet several conditions such as shortest path, maximum flow, and least cost. Graph theory is mathematical tool which enable to find out optimum layout for complicated network systems. In this study, applicability of graph theory to figure out optimum layout of irrigation pipeline networks was evaluated.

• Journal of applied mathematics & informatics
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• v.42 no.4
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• pp.761-775
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• 2024

A cycle in a graph G that contains all of its vertices is said to be the Hamiltonian cycle of that graph. A Hamiltonian graph is one that has a Hamiltonian cycle. This article discusses how to create a new network from an existing one, such as the Enhanced Honeycomb Network EHC(n), which is created by adding six new edges to each layer of the Honeycomb Network HC(n). Enhanced honeycomb networks have 9n² + 3n - 6 edges and 6n² vertices. For every perfect sub-Honeycombe topology, this new network features six edge disjoint Hamiltonian cycles, which is an advantage over Honeycomb. Its diameter is (2n + 1), which is nearly 50% lesser than that of the Honeycomb network. Using 3-bit grey code, we demonstrated that the Enhanced Honeycomb Network EHC(n) is Hamiltonian.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3216-3230
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• 2015

A hot-issued research topic in the workflow intelligence arena is the emerging topic of "workflow-supported organizational social networks." These specialized social networks have been proposed to primarily represent the process-driven work-sharing and work-collaborating relationships among the workflow-performers fulfilling a series of workflow-related operations in a workflow-supported organization. We can discover those organizational social networks, and visualize its analysis results as organizational knowledge. In this paper, we are particularly interested in how to visualize the degrees of closeness centralities among workflow-performers by proposing a graphical representation schema based on the Graph Markup Language, which is named to ccWSSN-GraphML. Additionally, we expatiate on the functional expansion of the closeness centralization formulas so as for the visualization framework to handle a group of workflow procedures (or a workflow package) with organizational workflow-performers.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.

• Journal of the Chungcheong Mathematical Society
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• v.35 no.3
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• pp.205-225
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• 2022

In this paper, we derive analytical closed results for the first (a, b)-KA index, the Sombor index, the modified Sombor index, the first reduced (a, b)-KA index, the reduced Sombor index, the reduced modified Sombor index, the second reduced (a, b)-KA index and the mean Sombor index mSO_α for the OTIS biswapped networks by considering basis graphs as path, wheel graph, complete bipartite graph and r-regular graphs. Network theory plays a significant role in electronic and electrical engineering, such as signal processing, networking, communication theory, and so on. A topological index (TI) is a real number associated with graph networks that correlates chemical networks with a variety of physical and chemical properties as well as chemical reactivity. The Optical Transpose Interconnection System (OTIS) network has recently received increased interest due to its potential uses in parallel and distributed systems.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.2
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• pp.65-74
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• 2002

Recently star networks are considered as attractive alternatives to the widely used hypercube for interconnection networks in parallel processing systems by many researchers. One of the fundamental communication problems on star graph networks is broadcasing In this paper we consider the broadcasting problems in star graph networks using wormhole routing. In wormhole routed system minimizing link contention is more critical for the system performance than the distance between two communicating nodes. We use Hamiltonian paths in star graph to set up link-disjoint communication paths We present a broadcast algorithm in n-dimensional star graph of N(=n!) nodes such that the total completion time is no larger than $([long_n n!]+1)$ steps where $([long_n n!]+1)$ is the lower bound This result is significant improvement over the previous n-1 step broadcasting algorithm.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• 전기의세계
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• v.17 no.2
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• pp.11-15
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• 1968

One of the most important methods used in the modern analysis of linear networks and systems is the signal flow graph technique, first introduced by S.J. Mason in 1953. In essence, the signal-flow graph technique is a graphical method of solving a set of simultaneous. It can, therefore, be regarded as an alternative to the substitution method or the conventional matrix method. Since a flow-graph is the pictorial representation of a set of equations, it has an obvious advantage, i.e., it describes the flow of signals from one point of a system to another. Thus it provides cause-and-effect relationship between signals. And it often significantly reduces the work involved, and also yields an easy, systematic manipulation of variables of interest. Mason's formula is very powerful, but it is applicable only when the desired quantity is the transmission gain between the source node and sink node. In this paper, author summarizes the signal-flow graph technique, and stipulates three rules for conversion of an arbitrary nonsource node into a source node. Then heuses the conversion rules to obtain various quantities, i.e., networks gains, functions and parameters, through simple graphical manipulations.