Structure Of Large Networks Research Articles

Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks

Real-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularityas a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values.

A Slimmer Network with Polymorphic and Group Attention Modules for More Efficient Object Detection in Aerial Images

Object detection is one of the core technologies in aerial image processing and analysis. Although existing aerial image object detection methods based on deep learning have made some progress, there are still some problems remained: (1) Most existing methods fail to simultaneously consider multi-scale and multi-shape object characteristics in aerial images, which may lead to some missing or false detections; (2) high precision detection generally requires a large and complex network structure, which usually makes it difficult to achieve the high detection efficiency and deploy the network on resource-constrained devices for practical applications. To solve these problems, we propose a slimmer network for more efficient object detection in aerial images. Firstly, we design a polymorphic module (PM) for simultaneously learning the multi-scale and multi-shape object features, so as to better detect the hugely different objects in aerial images. Then, we design a group attention module (GAM) for better utilizing the diversiform concatenation features in the network. By designing multiple detection headers with adaptive anchors and the above-mentioned two modules, we propose a one-stage network called PG-YOLO for realizing the higher detection accuracy. Based on the proposed network, we further propose a more efficient channel pruning method, which can slim the network parameters from 63.7 million (M) to 3.3M that decreases the parameter size by 94.8%, so it can significantly improve the detection efficiency for real-time detection. Finally, we execute the comparative experiments on three public aerial datasets, and the experimental results show that the proposed method outperforms the state-of-the-art methods.

Homothetic Behavior of Betweenness Centralities: A Multiscale Alternative Approach to Relate Cities and Large Regional Structures

Regional configuration can reveal important aspects about city sustainability, as local-regional interactions shape the evolution and inner geography of urban settlements. However, modelling these large-scale structures remains a challenge, due to their sheer size as physical objects. Despite recent improvements in processing power and computing methods, extensive time periods are still required for ordinary microprocessors to model network centralities in road-graphs with high element counts, connectivity and topological depth. Generalization is often the chosen option to mitigate time-constraints of regional network complexity. Nevertheless, this can impact visual representation and model precision, especially when multiscale comparisons are desired. Tests using Normalized Angular Choice (NACH), a Space Syntax mathematical derivative of Betweenness Centrality, found recursive visual similitudes in centrality spatial distribution when modelling distinct scaled map sections of the same large regional network structure. Therefore, a sort of homothetic behavior is identified, since statistical analyses demonstrate that centrality values and distributions remain rather consistent throughout scales, even when considering edge effects. This paper summarizes these results and considers homotheties as an alternative to extensive network generalization. Hence, data maps can be constructed sooner and more accurately as “pieces of a puzzle”, since each individual lesser scale graph possesses a faster processing time.

Synchronisation for multi‐network with two types of inter‐network coupling faults: pinning control effects

Investigation of simultaneous occurrence of permanent and/or recoverable faults of coupled nodes within different networks in a larger network structure is an appealing timely research topic. For the modelling of such a multi-network structure, the coupling terms in the same network and the coupling terms among different networks are described separately in order to explain clearly the multi-network feature here. The dimension-transformation matrix is used to deal with the mismatched dimensions of nodes in the different networks. Then a synchronisation controller is designed from the underlying theory of pinning control schemes. Furthermore, following the ideas of Lyapunov stability theory, a sufficient stability condition under the pinning control is derived which guarantees nodes in the same network synchronisation. Computer simulation results for a numerical case study of three coupled networks with nodes that possess rather complex non-linear dynamics demonstrate that the proposed controllers can enforce synchronisation despite different types of faults being occurred in the multi-network. It is also shown that the specific pinning control scheme is more effective than the random pinning.

Edge classification based on Convolutional Neural Networks for community detection in complex network

Community detection is a fundamental problem for many networks, and many methods have been proposed to resolve it. However, due to rapid increases in the scale and diversity of networks, the modular organization at the global level in many large networks is often extremely difficult to recognize. In this paper, we propose a new method based on deep learning on ground-truth communities, with the aim of revealing community structure in large real-world networks. The contributions of this paper are 1) proposing an edge-to-image (E2I) model that can transfer the edge structure to an image structure; 2) construction of a community network (ComNet) to classify the two types of edges, which are those in the same community and others between different communities; 3) making it easier to obtain local views of network communities by breadth-first search based on edge classification; and 4) merging preliminary communities with local modularity R, making it easy to optimize the community structure and obtain the final community structure of given networks. The experimental results show that the proposed edge classification method based on deep convolution neural networks can increase the accuracy of community structure evaluation compared with the existing methods in computer-generated networks and large-scale real-world networks.

A facile method for synthesizing NiS nanoflower grown on MXene (Ti3C2Tx) as positive electrodes for “supercapattery”

Transition metal sulfide is a good choice for supercapacitor anode materials. Herein, 3D NiS with flower-like shape was grown on MXene (Ti3C2TX) with one-step hydrothermal method. Connected nanoflakes in the composite formed a large void network structure, which favors the transfer of ions and charges. Compared with pure MXene, MXene/NiS composite exhibited higher specific capacity of 554.1 mAhg−1 (857.8 F g−1) at 1 A g−1 which is approximately 4 times greater than that MXene and superior cycle performance of 99.49% capacitance retained after 3000 cycles at 5 A g−1. The asymmetric supercapacitors were assemble in 2 M KOH alkaline solution with anode of MXene/NiS and cathode of G/AC (graphene/activated carbon). The ASCs device emerges a remarkable cycle performance of 97.7% after 3000 cycles and the energy density is up to 17.688 Wh kg−1 at a power density of 750 W kg−1. The small bulb (12 V, 2 W) can be lit for more than 100 s.

Scale-resolved analysis of brain functional connectivity networks with spectral entropy

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between a functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure. We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks. We show that network sparsification, in combination with motion correction algorithms, dramatically improves detection of large scale network structure.

Solution Combustion Synthesis of Porous MgO Nanostructures for Efficient Removal of Congo Red.

Porous MgO nanostructures were synthesized in large-scale by a simple solution combustion method using citric acid and glucose as the fuel. The resulting porous nanostructure powders were characterized by X-ray diffraction (XRD), scanning electronic microscopy (SEM), transmission electron microscopy (TEM), and N₂ adsorption-desorption isotherms. The SEM and TEM images demonstrated that the type of fuel used during the combustion synthesis strongly affects the morphology and structure. The use of glucose aggregated the structure with majority particle sizes in the range 3560 nm and a surface area of 40.55 m²g-1, while the use of citric acid results in a large network structure with smaller particle sizes (30-50 nm) and a larger surface area of 53.88 m²g-1. The two samples synthesized from glucose and citric acid exhibited excellent adsorption performance for Congo red with the maximum capacity reaching 1500.98 mg g-1 and 1638.08 mg g-1, respectively. These results indicate that the porous MgO synthesized by the solution combustion is a potential candidate for the removal of pollutant dye in water.

N- and S-rich covalent organic framework for highly efficient removal of indigo carmine and reversible iodine capture

Abstract Organic dye containing wastewater and nuclear waste have received more worldwide attention because of the significant environmental problems and the effects of radiation on health. Adsorption is considered to be an efficient, low-cost, and environmentally friendly method for pollutant removal. In this paper, we developed a novel covalent organic framework (BTT-TAPT-COF) adsorbent which contains abundant S and N active sites as well as extended π-conjugated structure. This COF shows high crystallinity, large surface area, and thermal stability. Due to the porous feature, existence of electron-rich heteroatoms as well as the large π-conjugated network structure, BTT-TAPT-COF exhibits excellent adsorptive performance for the removal of hazardous dye indigo carmine with an adsorption capacity of 547.33 mg g−1 and rapid reversible volatile iodine uptake with an adsorption capacity of 276 wt%. After five cycles, BTT-TAPT-COF almost retains the same iodine adsorption capacity with the original adsorbent.

Іноземний досвід мережевої взаємодії та можливості його застосування в Україні

The article is aimed at disclosing the peculiarities and adapting the experience of the Chinese company Huawei on the organizational integration of participants of Ukrainian networks in international markets. The article explores the experience and the market, economic, technological effectiveness of the Huawei’s integration strategy when entering international markets. It is substantiated that the main directions of such a strategy are: the use of direct connections of influence on the nature of the environment’s attitude to the company’s functioning policy through the development of cooperation with market leaders, local companies; the use of direct connections of influence on the investment environment, research, development and innovation activities in the host countries through investing in the development of science and research in these territories. The author proposes an adapted organizational scheme of network connections in order to implement the company’s integration strategy when large Ukrainian network structures enter international markets (based on the Huawei’s experience), which takes into account a number of advantages and problems that can be overcome with use of organizational management. It is defined that the aforementioned author’s proposal of organizational network-based international integration has a number of advantages related to: prospects for securing sales markets (or their expansion) by facilitating the development of other local foreign business entities (purchase of materials, goods, local services for the production of their own articles in these territories); investments in local science; facilitating the employment of the local population; facilitating the entry into new markets through the loyalty of competitors (through the use of their products, services in the production of their own articles (services)). It is proved that this scheme distinguishes itself by certain problematic aspects (the probability of loss of trade secrets in view of the impact of cooperation with foreign workers within the framework of new partner relationships of participants, the need for investments in scientific-technical activities in the host countries), but they can be overcome, and the benefits of its implementation are quite significant.

Speed up random walk by leveraging community affiliation information

Large online networks are most massive and opulent data sources these days. The inherent growing demands of analyses related data fetching conflict greatly with network providers’ efforts to protect their digital assets as well as users’ increasing awareness of privacy. Restrictions on web interfaces of online networks prevent third party researchers from gathering sufficient data and further global images of these networks are also hidden. Under such circumstances, only techniques like random walk approaches that can run under local neighborhood access will be adopted to fulfill large online network sampling tasks. Meanwhile, the presence of highly clustered community like structure in large networks leads to random walk’s poor conductance, causing intolerable and hard-to-foresee long mixing time before useful samples can be collected. With lack of techniques incorporate online network topology features being the context, in this paper we focus on taking use of community affiliation information that possibly comes with metadata when querying objects in online networks, and proposed a speeded version of random walk by raising the probability of inter-community edges being selected. Assuming the community structure is well established as promised, the community speeded random walk expects better conductance and faster convergence. Our method forces the sampler to travel rapidly among different communities that conquers the bottlenecks and thus the samples being collected are of higher quality. We also consider the scenario when community affiliation is not directly available, where we apply feature selection algorithms to select features as community.

Krylov Subspace Approximation for Local Community Detection in Large Networks

Community detection is an important information mining task to uncover modular structures in large networks. For increasingly common large network datasets, global community detection is prohibitively expensive, and attention has shifted to methods that mine local communities, i.e., identifying all latent members of a particular community from a few labeled seed members. To address such semi-supervised mining task, we systematically develop a local spectral (LOSP) subspace-based community detection method, called LOSP. We define a family of LOSP subspaces based on Krylov subspaces, and seek a sparse indicator for the target community via an ℓ 1 norm minimization over the Krylov subspace. Variants of LOSP depend on type of random walks with different diffusion speeds, type of random walks, dimension of the LOSP subspace, and step of diffusions. The effectiveness of the proposed LOSP approach is theoretically analyzed based on Rayleigh quotients, and it is experimentally verified on a wide variety of real-world networks across social, production, and biological domains, as well as on an extensive set of synthetic LFR benchmark datasets.

Dyadic associations reveal clan size and social network structure in the fission–fusion society of spotted hyaenas

AbstractEstimating the size and dynamics of populations is of paramount importance in ecology. In species with uniquely marked individuals, capture–recapture methods can be used to establish population size and to explore associations between individuals. However, very few studies have used cameras traps to focus on group composition in social carnivores, despite being of particular interest in species characterised by “fission–fusion” formation of sub‐groups. Here, we provide estimates of (a) population size, (b) density, (c) clan size, (d) association patterns and (e) social network structure in spotted hyaenas (Crocuta crocuta) based on images from camera traps deployed at waterholes on Ongava Game Reserve (northern Namibia). In a 15 week study period, we identified 32 individuals. Dyadic associations and the resulting social network showed that all but two hyaenas associated directly or indirectly with each other, indicating the presence of one clan of at least 30 individuals, resulting in a density of 8.1 hyaenas/100 km2. We found a very high variability in the tendency of individuals to associate with others. This study confirms a highly dynamic fission–fusion society in spotted hyaenas. We argue that camera traps can provide relevant insights into large carnivore social network structure where associations between individuals are difficult to observe directly.

From Louvain to Leiden: guaranteeing well-connected communities

Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.

REMODEL: Rethinking Deep CNN Models to Detect and Count on a NeuroSynaptic System.

In this work, we perform analysis of detection and counting of cars using a low-power IBM TrueNorth Neurosynaptic System. For our evaluation we looked at a publicly-available dataset that has overhead imagery of cars with context present in the image. The trained neural network for image analysis was deployed on the NS16e system using IBM's EEDN training framework. Through multiple experiments we identify the architectural bottlenecks present in TrueNorth system that does not let us deploy large neural network structures. Following these experiments we propose changes to CNN model to circumvent these architectural bottlenecks. The results of these evaluations have been compared with caffe-based implementations of standard neural networks that were deployed on a Titan-X GPU. Results showed that TrueNorth can detect cars from the dataset with 97.60% accuracy and can be used to accurately count the number of cars in the image with 69.04% accuracy. The car detection accuracy and car count (–/+ 2 error margin) accuracy are comparable to high-precision neural networks like AlexNet, GoogLeNet, and ResCeption, but show a manifold improvement in power consumption.

An approach based on mixed hierarchical clustering and optimization for graph analysis in social media network: toward globally hierarchical community structure

As the massive size of contemporary social networks poses a serious challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities, we develop, in this manuscript, an approach used to discover hierarchical community structure in large networks. The introduced hybrid technique combines the strengths of bottom-up hierarchical clustering method with that of top-down hierarchical clustering. In fact, the first approach is efficient in identifying small clusters, while the second one is good at determining large ones. Our mixed hierarchical clustering technique, based on the assumption that there exists an initial solution composed of k classes and the combination of the two previously mentioned methods, does not the change of the number of partitions, modifies the repartition of the initial classes. At the end of the introduced clustering process, a fixed point, representing a local optimum of the cost function which measures the degree of importance between two partitions, is obtained. Consequently, the introduced combined model leads to the emergence of local community structure. To avoid this local optimum and detect community structure converged to the global optimum of the cost function, the detection of community structures, in this study, is not considered only as a clustering problem, but as an optimization issue. Besides, a novel mixed hierarchical clustering algorithm based on swarms intelligence is suggested for identifying community structures in social networks. In order to validate the proposed method, performances of the introduced approach are evaluated using both real and artificial networks as well as internal and external clustering evaluation criteria.

A New Algorithm for Learning Large Bayesian Network Structure From Discrete Data

Learning the structure of Bayesian networks (BNs) from high dimensional discrete data is common nowadays but a challenging task, due to the large parameter space, the acyclicity constraint placed on the graphical structures and the difficulty in searching for a sparse structure. In this article, we propose a sparse structure learning algorithm (SSLA) to solve this problem. The algorithm uses the negative log-likelihood function of multi-logit regression as a loss function, adding the adaptive group lasso as a penalty term for sparsity, with a new penalty term to ensure that the learned graph is a directed acyclic graph. A block coordinate descent algorithm (BCD) combining with the alternating direction multiplier method (ADMM) algorithm is developed to solve the proposed model. The learned graph is proved theoretically to be a Bayesian network. In order to evaluate the proposed SSLA and compare with its competitors, we conducted intensive simulation studies and applied them to the benchmark Bayesian networks. The results indicate that the SSLA is superior to the hill climbing (HC) algorithm, the CD algorithm and the BFO-B algorithm respectively, and is competitive with K2 algorithm when the order of the nodes is given.

Multi-level fuzzy overlapping community detection algorithm for weighted networks

To understand the hierarchical structure of large networks we propose a multi-level community detection algorithm. Each level consists of a set of fuzzy overlapping communities where a node can belong to multiple communities associated with belonging membership degrees. Our algorithm works for weighted and unweighted networks as well. For each level of hierarchy, it identifies the centres of potential communities and iteratively expands them to form the final communities. A new vector representation of the nodes is proposed and used in the centre's expansion process and in the computation of the belonging degrees. Communities detected at a given level serve as super nodes while identifying the overlapping communities of a higher level. Experiments achieved on the well-known LFR benchmark showed high performance which is measured by the normalised mutual information (NMI). Unlike existing methods, our algorithm shows good and stable performance when varying the number of communities of the overlapping nodes.

Small Business in Digital Economy

Business plays a special role in the mechanism of sustainable economic development, since it provides not only the realization of innovative potential, but also the creation of decentralized jobs, as well as additional revenues to budgets of different levels. Periods of economic instability for small business at the local level are a big challenge, because unlike large network structures, small business does not have the ability to diversify risks due to economies of scale. Changing the vector of the national economy development has a special impact on small business, which in developed countries is a driver of economic development. Thus, the digitalization of the economic system entails fundamental changes in the processes of value-added formation: industries are being modernized, trade and procurement procedures, related financial and logistics operations are changing, and consumption patterns are changing against the background of penetration of information technologies. The article considers two key aspects: “small business”, “digitalization of the economic system”. The concept of “digital economy” is considered, its key elements, distinctive features, level of development in various countries are defined. The authors also clarify the distinctive features and structure of small business in the Russian Federation. A comparative analysis of the share of small business in some countries is given, and a model for the functioning of small business at the regional level is described. The authors identify the problems and prospects for the development of small business in a digital economy. The article tackles the issues of the impact of the digital economy on the development of small business and the role of education in this process. The article formulates the main trends of digital transformation that have a significant role for small business at the regional level; the essence of this influence is detailed, possible barriers, new challenges and prospects for the development of small business are indicated.

Application Communities Detection in Network

The continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose a new type of network community—the application community—which can help understand large network structure and find anomaly network behavior. To detect such a community, a method is proposed whose first step is aggregating the nodes according to their topological relationships of the communication. It then clusters different application nodes according to the communication behavior modes in the same topological partition. Empirical results show that this method can accurately detect communities of different applications without any prior knowledge. In addition, it can identify the communities more accurately than other methods. Thus, this research greatly benefits the administration of IoT and cyber security.