Embedding complex networks in a low dimensional Euclidean space based on vertex dissimilarities

Elsevier 2012 PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIO Vol.391 No.20

<P><B>Abstract</B></P><P>We propose a method for representing vertices of a complex network as points in a Euclidean space of an appropriate dimension. To this end, we first adopt two widely used quantities as the measures for the dissimilarity between vertices. The dissimilarity is then transformed into its corresponding distance in a Euclidean space via the non-metric multidimensional scaling. We applied the proposed method to real-world as well as models of complex networks. We empirically found that real-world complex networks were embedded in a Euclidean space of relatively lower dimensions and the configuration of vertices in the space was mostly characterized by the self-similarity of a multifractal. In contrast, by applying the same scheme to the network models, we found that, in general, higher dimensions were needed to embed the networks into a Euclidean space and the embedding results usually did not exhibit the self-similar property. From the analysis, we learn that the proposed method serves a way not only to visualize the complex networks in a Euclidean space but to characterize the complex networks in a different manner from conventional ways.</P> <P><B>Highlights</B></P><P>► The proposed method overcomes a conventional way of studying self-similarity. ► The real complex networks were embedded in a Euclidean space. ► The configuration of vertices was characterized by the multifractal. ► Model networks did not exhibit the self-similar property.</P>

The complex viscosity of polymer carbon nanotubes nanocomposites as a function of networks properties

Yasser Zare,Vesna Mišković‑Stanković,Kyong Yop Rhee 한국탄소학회 2019 Carbon Letters Vol.29 No.5

Cross model correlates the dynamic complex viscosity of polymer systems to zero complex viscosity, relaxation time and power-law index. However, this model disregards the growth of complex viscosity in nanocomposites containing filler networks, especially at low frequencies. The current paper develops the Cross model for complex viscosity of nanocomposites by yield stress as a function of the strength and density of networks. The predictions of the developed model are compared to the experimental results of fabricated samples containing poly(lactic acid), poly(ethylene oxide) and carbon nanotubes. The model’s parameters are calculated for the prepared samples, and their variations are explained. Additionally, the significances of all parameters on the complex viscosity are justified to approve the developed model. The developed model successfully estimates the complex viscosity, and the model’s parameters reasonably change for the samples. The stress at transition region between Newtonian and power-law behavior and the power-law index directly affects the complex viscosity. Moreover, the strength and density of networks positively control the yield stress and the complex viscosity of nanocomposites. The developed model can help to optimize the parameters controlling the complex viscosity in polymer nanocomposites.

The complex viscosity of polymer carbon nanotubes nanocomposites as a function of networks properties

Zare Yasser,Mišković-Stanković Vesna,이경엽 한국탄소학회 2019 Carbon Letters Vol.29 No.5

Cross model correlates the dynamic complex viscosity of polymer systems to zero complex viscosity, relaxation time and power-law index. However, this model disregards the growth of complex viscosity in nanocomposites containing filler networks, especially at low frequencies. The current paper develops the Cross model for complex viscosity of nanocomposites by yield stress as a function of the strength and density of networks. The predictions of the developed model are compared to the experimental results of fabricated samples containing poly(lactic acid), poly(ethylene oxide) and carbon nanotubes. The model’s parameters are calculated for the prepared samples, and their variations are explained. Additionally, the significances of all parameters on the complex viscosity are justified to approve the developed model. The developed model successfully estimates the complex viscosity, and the model’s parameters reasonably change for the samples. The stress at transition region between Newtonian and power-law behavior and the power-law index directly affects the complex viscosity. Moreover, the strength and density of networks positively control the yield stress and the complex viscosity of nanocomposites. The developed model can help to optimize the parameters controlling the complex viscosity in polymer nanocomposites.

Detecting community structure in complex networks using an interaction optimization process

Kim, Paul,Kim, Sangwook Elsevier 2017 PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIO Vol.465 No.-

<P><B>Abstract</B></P> <P>Most complex networks contain community structures. Detecting these community structures is important for understanding and controlling the networks. Most community detection methods use network topology and edge density to identify optimal communities; however, these methods have a high computational complexity and are sensitive to network forms and types. To address these problems, in this paper, we propose an algorithm that uses an interaction optimization process to detect community structures in complex networks. This algorithm efficiently searches the candidates of optimal communities by optimizing the interactions of the members within each community based on the concept of greedy optimization. During this process, each candidate is evaluated using an interaction-based community model. This model quickly and accurately measures the difference between the quantity and quality of intra- and inter-community interactions. We test our algorithm on several benchmark networks with known community structures that include diverse communities detected by other methods. Additionally, after applying our algorithm to several real-world complex networks, we compare our algorithm with other methods. We find that the structure quality and coverage results achieved by our algorithm surpass those of the other methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Improving the technique for detecting community structures is important for understanding and controlling complex networks. </LI> <LI> Most community detection methods have a high computational complexity and are sensitive to network forms and types. </LI> <LI> We propose an algorithm that uses an interaction optimization process to detect community structures in complex networks. </LI> <LI> We find that the structure quality and coverage resulting from our algorithm surpass the results of other methods. </LI> </UL> </P>

Second Generation Neural Network for Two Dimensional Problems

Manmohan Shukla,B. K. Tripathi 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11

Neurocomputing in complex domain has yielded second generation neural networks. The neural network, which is based on complex value, contains different layers. The attributes of these layers are biases, weights, inputs and outputs. These attributes are also complex numbers. The signal processing, speech processing, learning and prediction of motion on plane are few areas in which complex domain neurocomputing is applied., since in the above said areas, the inputs and outputs are represented by complex values. It has been observed that the neural network with complex value can easily perform the transformation of geometric figures. The examples of transformations are rotation, parallel displacement of straight lines and circles. The neural network can extend to complex domain by the application of transformation. A number in complex domain is composed of different entities i.e. two real numbers and phase information. The two real numbers and phase information of any point on plane is naturally embedded in this number.

딥러닝 예측 결과 정보를 적용하는 복합 미생물배양기를 위한 딥러닝 구조 개발

김홍직,이원복,이승호 한국전기전자학회 2023 전기전자학회논문지 Vol.27 No.1

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network

Gengxin Sun,Sheng Bin,Yixin Zhou 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.5

Online social networks such as Twitter and Facebook are becoming popular form of social information networks. There are frequently many kinds of relationships in an online social network. Complex Network acting as one kind of big data technologies is often used to analyze users' social activities. By studying the Douban network, which is a representative multi-relationship online social network in China, big data of friendship relationship and book comments similar relationship are crawled through network topology measurement software, from the perspective of topological characteristics of complex network, the basic topologies of the two relationship networks constructed individually by the two relationships are analyzed. Based on these, a multi-relationship online social network based on Multi-subnet Composited Complex Network Model is constructed through loading book comments similar relationship subnet to follower relationship subnet, and accurate understanding of topologies of Douban multi-relationship network is obtained. These findings provide a deep understanding on the evolution of multi-relationship online social network, and can provide guidelines on how to build an efficient multi-relationship online social network evolution model.

Characteristics of microRNA co-target networks

Elsevier 2011 PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIO Vol.390 No.14

<P><B>Abstract</B></P><P>The database of microRNAs and their predicted target genes in humans were used to extract a microRNA co-target network. Based on the finding that more than two miRNAs can target the same gene, we constructed a microRNA co-target network and analyzed it from the perspective of the complex network. We found that a network having a positive assortative mixing can be characterized by small-world and scale-free characteristics which are found in most complex networks. The network was further analyzed by the nearest-neighbor average connectivity, and it was shown that the more assortative a microRNA network is, the wider the range of increasing average connectivity. In particular, an assortative network has a power-law relationship of the average connectivity with a positive exponent. A percolation analysis of the network showed that, although the network is diluted, there is no percolation transition in the network. From these findings, we infer that the microRNAs in the network are clustered together, forming a core group. The same analyses carried out on different species confirmed the robustness of the main results found in the microRNA networks of humans.</P> <P><B>Highlights</B></P><P>► We construct microRNA co-target networks from the miRBase database. ► We investigate characteristics of microRNA networks. ► Positive assortative mixing networks can be characterized by small-world and scale-free attributes. ► Although the network is diluted, there is no percolation transition in the networks. ► We infer that the microRNAs in the network are clustered together to form a core group.</P>

Spatial distribution of access diversity on urban road networks

Lee Minjin,Kim Beom Jun 한국물리학회 2021 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.79 No.5

The road network in an urban area plays an essential role in mediating the mobility of people and goods and in supporting various social and economic activities. In urban road networks, accessibility is one of the crucial aspects of evaluating their performance. The existing measures from urban science focus on practical user-based accessibility utilizing various data, including human mobility patterns, transport networks, and land use layout. While such measures can present a comprehensive picture of accessibility, they hardly describe how a single factor, essentially the structural and topological patterns of road networks, determines accessibility level. Meanwhile, the random-walk-based accessibility used for various complex networks can effectively analyze structurewise accessibility. However, we claim that the frequently used version of the random-walk-based accessibility only considers the topology of networks, and thus often neglects important geometric information such as the spatial physical distances in road networks. In this study, we modify the existing random-walk-based accessibility method to be more applicable for road networks by including information of geometric distance. To evaluate the effect of the distance information on accessibility, we analyze the empirical road networks of three global metropolitan cities with different structures. We find that our spatial accessibility captures the disparity in spatial and topological structures and highlights spatially accessible areas in different cities. Especially, our methodology brings a dramatic change in the cities with heterogeneous road patterns. We also compare the spatial distribution patterns of our proposed accessibility in the three cities and discuss how road structures are related to the accessibility distributions.

글로벌 금융위기와 세계경제 거버넌스 변화: 복합네트워크론의 유용성과 한계

김치욱 세종연구소 2011 국가전략 Vol.17 No.2

The 2008 global financial crisis has arguably dealt a blow to the West's political economic leadership in the world, but boosted China and emerging economies up to the new powerhouses. Reshuffling the distribution of economic capabilities among major powers entails a more visible change in global economic governance. From the perspective of complex networks, this article analyzes the key characteristics of a new mode of economic governance. Such 'complex network governance' is a type of governance in which, along with traditional bilateral inter-state relationships, multistakeholders such as intergovernmental organizations and networks, and transnational networks share responsibility and authority. In particular, the G20, officially recognized as a focal point of world economic cooperation, has promoted a networked governance of world economy by functionally connecting the existing bilateral and multilateral, formal and informal, state actors. The G20 has some weaknesses as a facilitator of complex network governance in that it has allowed a very limited involvement of non-state actors and that its institutional sustainability is still shaky. Korea's economic diplomacy needs to focus on maximizing connectedness within the networks and utilizing the overlapped and embedded governance networks. 글로벌 금융위기 이후 서구의 정치경제적 지도력에 대한 회의론이 비등하고 경제력의 중심이 중국과 아시아 및 신흥시장으로 이동하면서 세계경제 거버넌스에 중요한 변화가 가시화되었다. 이 논문은 복합네트워크론의 시각으로 글로벌 경제 거버넌스의 주요 특징을 분석했다. 복합네트워크 거버넌스는 국민국가 간 양자관계뿐 아니라, 다자적인 차원에서 정부간 국제기구, 정부네트워크, 그리고 초정부 네트워크 등이 일정한 책임과 권한을 공유하는 시스템이다. 특히 세계경제협력의 초점으로 공인된 G20정상회의는 기존의 양자 및 다자, 공식 또는 비공식 국가 행위자들을 기능적 연결망으로 꿰면서 거버넌스의 복합네트워크화를 촉진했다. 다만, G20 프로세스는 시민사회 등 비국가행위자의 참여가 제한적이고 제도로서 지속가능성이 불확실하다는 점에서 복합네트워크의 촉진자로서 일정한 한계점을 지닌다. 정보화와 세계화의 흐름을 타고 가속화될 복합네트워크 시기의 한국 외교는 네트워크 내 연결성을 극대화하면서, 네트워크 간의 중첩성과 중복성을 최대한 활용하는 전략이 요구된다.