Local Network Structure Research Articles

Robustness measurement of multiplex networks based on multiplex motifs

Multiplex network is a classic multilayer network model that can be used to model complex systems in the real world. Previous measures of network robustness mainly focused on the low-order characteristics of network topology, and the existence of motifs in the network is ignored. As simple building blocks of multiplex networks, multiplex motifs are indispensable tools to understand the structure of local networks. Therefore, from a mesoscopic perspective, we propose an index called normalized motif entropy to measure the robustness of multiplex networks under malicious attacks, and the effectiveness of our method is verified in real multiplex networks.

Integration of migration and attention flow data to reveal association of virtual–real dual intercity network structure

The study of intercity network structure from the perspective of flow space can effectively reveal the connection patterns, hierarchical laws, and spatial organization of cities. However, current studies mostly focus on a single real or virtual space, neglecting the comprehensive perspective. Furthermore, the research subject of network relationships is usually the complex original aligned network rather than the spatially cohesive network structure. In this study, spatial network structures are constructed and extracted based on the migration flow and attention flow, which represent people's objective behavior and subjective will, respectively. This study then reveals the association between virtual and real networks from three aspects: node location, node attributes, and local network structure characteristics. Results indicate that compared with the real network structure, the virtual network structure has a strong same-level node agglomeration and a significant node size difference. Spatial dislocations are observed in the high association of the various measures of China's real and virtual network structures. The degree of differences in network structures between virtual and real spaces varies spatially. This paper reveals the dynamics of urban development in different spaces, helping build a human-centered, high-quality space governance system for virtual and real spaces.

Characterization of intracellular membrane structures derived from a massive expansion of endoplasmic reticulum (ER) membrane due to synthetic ER-membrane-resident polyproteins.

The endoplasmic reticulum (ER) is a dynamic organelle that is amenable to major restructuring. Introduction of recombinant ER-membrane-resident proteins that form homo oligomers is a known method of inducing ER proliferation: interaction of the proteins with each other alters the local structure of the ER network, leading to the formation large aggregations of expanded ER, sometimes leading to the formation of organized smooth endoplasmic reticulum (OSER). However, these membrane structures formed by ER proliferation are poorly characterized and this hampers their potential development for plant synthetic biology. Here, we characterize a range of ER-derived membranous compartments in tobacco and show how the nature of the polyproteins introduced into the ER membrane affect the morphology of the final compartment. We show that a cytosol-facing oligomerization domain is an essential component for compartment formation. Using fluorescence recovery after photobleaching, we demonstrate that although the compartment retains a connection to the ER, a diffusional barrier exists to both the ER and the cytosol associated with the compartment. Using quantitative image analysis, we also show that the presence of the compartment does not disrupt the rest of the ER network. Moreover, we demonstrate that it is possible to recruit a heterologous, bacterial enzyme to the compartment, and for the enzyme to accumulate to high levels. Finally, transgenic Arabidopsis constitutively expressing the compartment-forming polyproteins grew and developed normally under standard conditions.

Role of interneuron subtypes in controlling trial-by-trial output variability in the neocortex

Trial-by-trial variability is a ubiquitous property of neuronal activity in vivo which shapes the stimulus response. Computational models have revealed how local network structure and feedforward inputs shape the trial-by-trial variability. However, the role of input statistics and different interneuron subtypes in this process is less understood. To address this, we investigate the dynamics of stimulus response in a cortical microcircuit model with one excitatory and three inhibitory interneuron populations (PV, SST, VIP). Our findings demonstrate that the balance of inputs to different neuron populations and input covariances are the primary determinants of output trial-by-trial variability. The effect of input covariances is contingent on the input balances. In general, the network exhibits smaller output trial-by-trial variability in a PV-dominated regime than in an SST-dominated regime. Importantly, our work reveals mechanisms by which output trial-by-trial variability can be controlled in a context, state, and task-dependent manner.

Structured multiagent decision-making in information diffusion: The model and dynamics

In recent years, studies in many fields have widely explored diffusion in social networks, which refers to the collective behavior of a set of autonomous social actors who interact with something (such as opinions, viruses, or innovations) in social networks. Many researchers have utilized a multiagent method to explore the effects of social actor characteristics and behavioral complexity on information diffusion. This method performs well in modeling the complex characteristics and decision-making of social actors. Although this subject has been intensively studied, analyses have typically been limited to concerns about the effects of entire social network structures, such as the small-world and scale-free aspects. Few empirical information diffusion studies have systematically investigated causal effects and decision-making mechanisms under the constraints of the interaction structures of agents. In practice, we observed that the decision-making of each agent in information diffusion was significantly influenced and constrained by its contextual network structures. To solve this problem, this study utilizes the cause–effect graph method to model the interaction structures of agents in social networks and presents agent decision-making models under the constraints of local network structures in information diffusion. The proposed models and methods can be used to effectively analyze the mechanism of the influence of structured interaction relations of agents on the process of decision-making and diffusion results. Moreover, the presented decision-making model can comprehensively consider the factors of social positions of heterogeneous agents, interaction strengths, and diffusion strategies, and thus, can better match the features of real-world influence diffusion in social networks.

Graph Neural Network-Based Design Decision Support for Shared Mobility Systems

Abstract Emerging shared mobility systems are gaining popularity due to their significant economic and environmental benefits. In this paper, we present a network-based approach for predicting travel demand between stations (e.g., whether two stations have sufficient trips to form a strong connection) in shared mobility systems to support system design decisions. In particular, we answer the research question of whether local network information (e.g., the network neighboring station’s features of a station and its surrounding points of interest (POI), such as banks, schools, etc.) would influence the formation of a strong connection or not. If so, to what extent do such factors play a role? To answer this question, we propose using graph neural networks (GNNs), in which the concept of network embedding can capture and quantify the effect of local network structures. We compare the results with a regular artificial neural network (ANN) model that is agnostic to neighborhood information. This study is demonstrated using a real-world bike sharing system, the Divvy Bike in Chicago. We observe that the GNN prediction gains up to 8% higher performance than the ANN model. Our findings show that local network information is vital in the structure of a sharing mobility network, and the results generalize even when the network structure and density change significantly. With the GNN model, we show how it supports two crucial design decisions in bike sharing systems, i.e., where new stations should be added and how much capacity a station should have.

Effect of Weld Design of Transition Layer on Microstructure and Properties of Welded Joints of S32304/Q390C Composites

Due to the large difference in physical and chemical properties between the substrate and the cladding material, the welding of composite materials is much more difficult than that of single materials. In our work, S32304/Q390C composite material was considered as the research object. By adjusting the welding parameters, two kinds of joint geometry were obtained, namely, the transition layer weld lower (joint A) and higher the composite material interface (joint B). We studied the influence of the transition layer weld on the microstructure and properties of welded joints. The microstructure of the transition layer weld, the distribution of elements, the Schmidt factor of the interface between the transition layer and base layer weld, and the tensile strength of the joint were evaluated. The results show that with the increase of welding heat input, the microstructure of the transition layer weld changes from austenite and skeleton ferrite to austenite and lathy ferrite and austenite and acicular ferrite, while ferrite grows towards the weld center, showing a dendritic shape and a local network structure. At the side of the base layer weld of the interface between the transition and the base layer weld, the thickness of the low-carbon-content layer increased from 100 μm to 150 μm. Iron, chromium, and nickel elements on both sides of the interface were diffused, and the thickness of the diffusion layer increased from 3 μm to 10 μm. The tensile strength values of joints A and B were 648 MPa and 668 MPa, respectively, and the Schmidt factor values were 0.446 and 0.454, respectively. Combination with the analysis of the fracture morphology showed that when the transition layer weld was higher than the interface of the composite plate, the joint had better plastic deformation ability and higher tensile strength.

CTL-DIFF: Control Information Diffusion in Social Network by Structure Optimization

A critical side effect of online social networks' flourishing is fast-spreading rumors on the Internet, making the information diffusion control on social networks a fundamental requirement. While information diffusion control has received extensive attention at the global level, there have been fewer user-level diffusion control studies under minimal budget. In this article, we study the information diffusion at the user level and propose a diffusion control method based on gradient information to generate an optimized network structure, namely ConTroL information DIFFusion (CTL-DIFF). CTL-DIFF targets a user through subtle modifications of its local network structure. It first selects the edges with the largest absolute gradient based on the prediction model to optimize the original network's structure. It then employs several prediction methods to verify whether the target user's social action status is controlled. CTL-DIFF achieves state-of-the-art control performance with a minimum budget, comparing with five baselines based on edge centrality strategies on four real-world datasets. We extend the diffusion control from user-level to global-level, comparing with four baselines on three datasets. Experimental results show that CTL-DIFF can effectively control information diffusion in the global social network by identifying and controlling the most influential users.

Evolution mechanism of the local network structure recorded in distribution of distances between neighbors of each vertex

The explosive development of research on real networks have led to the proposal of many mathematical network models explaining properties shared in the real networked systems. However, considering that different models can similarly reproduce the typical properties of networks, the estimation of the evolutionary process is more essential for appropriate network modeling than is the description of current network structures. In this paper, we propose a new measure “distribution of distances between neighbors (DDN) of each vertex” to investigate network development. We first examine several model networks, including growth models based on the attachment rule and on the local rule and their hybrid models, and show that the DDN profiles of these networks accurately reflect the differences in growth mechanisms. In particular, for the local rule model, the dependence of the mean DDN of vertices on the vertex degree records the evolution of the local network structure. The application of the same analysis to several real networks show that they share the characteristics of DDN profiles with the local rule and hybrid models. These results imply that these model networks appropriately describe the evolutionary processes of real networks. In addition, we examine the characteristics of DDN specific to fractal and non-fractal networks, and show that the presence or absence of fractality of some real networks can be explained by the influence of long-range edges on the local rule. An exception to this is the co-authorships network, which is expected to be generated by connecting several fractal sub-networks.

Investigation of communication parameters based on monitoring and statistical modelling

For the effective construction of a network infrastructure, it is recommended to carry out a preliminary investigation of communications and information processing by using suitable methods and tools. In this regard, the article deals with an approach applied in the investigation of network communications in a local network structure based on program monitoring and statistical analysis of the measured data. To specify the research object and the means used, the organization of the network structure is presented, and a brief discussion of sample monitoring programs is made. The organization for the conducted program monitoring is presented and main factors of network communications are defined. Statistical modelling approach is used to investigate selected parameters of communication in the network environment. Some obtained statistical assessments are presented which permit to make conclusions for access and communication organization.

Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks

As the use of digital currencies, such as cryptocurrencies, increases in popularity, phishing scams and other cybercriminal activities on blockchain platforms (e.g., Ethereum) have also risen. Current methods of detecting phishing in Ethereum focus mainly on the transaction features and local network structure. However, these methods fail to account for the complexity of interactions between edges and the handling of large graphs. Additionally, these methods face significant issues due to the limited number of positive labels available. Given this, we propose a scheme that we refer to as the Bagging Multiedge Graph Convolutional Network to detect phishing scams on Ethereum. First, we extract the features from transactions and transform the complex Ethereum transaction network into three simple inter-node graphs. Then, we use graph convolution to generate node embeddings that leverage the global structural information of the inter-node graphs. Further, we apply the bagging strategy to overcome the issues of data imbalance and the Positive Unlabeled (PU) problem in transaction data. Finally, to evaluate our approach’s effectiveness, we conduct experiments using actual transaction data. The results demonstrate that our Bagging Multiedge Graph Convolutional Network (0.877 AUC) outperforms all of the baseline classification methods in detecting phishing scams on Ethereum.

Network Embedding via Deep Prediction Model

Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and sparsity of the data. The local and global structure of the real-world networks can be reflected by dynamical transfer behaviors among nodes. This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models. We first design a degree-weight biased random walk model to capture the transfer behaviors on the network. Then a deep network embedding method is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into conventional deep prediction models, including Long Short-Term Memory Network and Recurrent Neural Network, to utilize the sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various datasets including social networks, citation networks, biomedical network, collaboration network and language network. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization, classification, reconstruction and link recovery, and achieve promising performance compared with state-of-the-arts.

Message Passing Neural Networks Improve Prediction of Metabolite Authenticity.

Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely react with protein and DNA and prompt drug candidate attrition or market withdrawal. Previously developed models help understand how these enzymes modify molecule structure by predicting sites of metabolism or characterizing formation of metabolite-biomolecule adducts. However, the majority of reactive metabolites are formed by multiple metabolic steps, and understanding the progenitor molecule's network-level behavior necessitates an integrative approach that blends multiple site of metabolism and structure inference models. Our previously developed tool, XenoNet 1.0, generates metabolic networks, where nodes are molecules and weighted edges are metabolic transformations. We extend XenoNet with a bidirectional message passing neural network that integrates edge feature information and local network structure using edge-conditioned graph convolutions and jumping knowledge to predict the authenticity of inferred Phase I metabolite structures. Our model significantly outperformed prior work and algorithmic baselines on a data set of 311 networks and 6606 intermediates annotated using a chemically diverse set of 20 736 individual in vitro and in vivo reaction records accounting for 92.3% of all human Phase I metabolism in the Accelrys Metabolite Database. Cross-validated predictions resulted in area under the receiver operating characteristic curves of 88.5% and 87.6% for separating experimentally observed and unobserved metabolites at global and network levels, respectively. Further analysis verified robustness to networks of varying depth and breadth, accurate detection of metabolites, such as d,l-methamphetamine, that are experimentally observed or unobserved in different network contexts, extraction of important metabolic subnetworks, and identification of known bioactivation pathways, such as for nimesulide and terbinafine. By exploiting network structures, our approach accurately suggests unreported metabolites for experimental study and may rationalize modifications for avoiding deleterious pathways antecedent to reactive metabolite formation.

Local social network structure and promotion effectiveness in social commerce

PurposeDrawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.Design/methodology/approachThe authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.FindingsLocal social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.Originality/valueThe paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

Emergence of allostery through reorganization of protein residue network architecture.

Despite more than a century of study, consensus on the molecular basis of allostery remains elusive. A comparison of allosteric and non-allosteric members of a protein family can shed light on this important regulatory mechanism, and the bacterial biotin protein ligases, which catalyze post-translational biotin addition, provide an ideal system for such comparison. While the Class I bacterial ligases only function as enzymes, the bifunctional Class II ligases use the same structural architecture for an additional transcription repression function. This additional function depends on allosterically activated homodimerization followed by DNA binding. In this work, we used experimental, computational network, and bioinformatics analyses to uncover distinguishing features that enable allostery in the Class II biotin protein ligases. Experimental studies of the Class II Escherichia coli protein indicate that catalytic site residues are critical for both catalysis and allostery. However, allostery also depends on amino acids that are more broadly distributed throughout the protein structure. Energy-based community network analysis of representative Class I and Class II proteins reveals distinct residue community architectures, interactions among the communities, and responses of the network to allosteric effector binding. Bioinformatics mutual information analyses of multiple sequence alignments indicate distinct networks of coevolving residues in the two protein families. The results support the role of divergent local residue community network structures both inside and outside of the conserved enzyme active site combined with distinct inter-community interactions as keys to the emergence of allostery in the Class II biotin protein ligases.

FedProc: Prototypical contrastive federated learning on non-IID data

Federated learning (FL) enables multiple clients to jointly train high-performance deep learning models while maintaining the training data locally. However, it is challenging to accomplish this form of efficient collaborative learning when all the clients’ local data are not independent and identically distributed (i.e., non-IID). Despite extensive efforts to address this challenge, the results for image classification tasks remain inadequate. In this paper, we propose FedProc: prototypical contrastive federated learning. The core idea of this approach is to utilize the prototypes as global knowledge to correct the drift of each client’s local training. Specifically, we designed a local network structure and a global prototype contrast loss to regulate the training of the local model. These efforts make the direction of local optimization consistent with the global optimum such that the global model achieves good performance on non-IID data. Evaluative studies supported by theoretical significance demonstrate that FedProc improves accuracy by 1.6% to 7.9% with an acceptable computational cost compared to state-of-the-art federated learning methods.

Different influences of phylogenetically conserved and independent floral traits on plant functional specialization and pollination network structure.

Plant specialization and pollination network structure play important roles in community assembly. Floral traits can mediate plant-pollinator interactions and thus have important impacts on nestedness and modularity of pollination network. When such traits are phylogenetically conserved, therefore, phylogeny and traits should predict network structure to similar degrees. Moreover, conserved network structures were also found attributed to pollination syndrome or pollination system. However, we still know little about the relation between pollination syndrome and pollination network, especially under a phylogenetic framework. Herein, we established a phylogenetic framework including five floral traits (flower density, floral size, floral shape, floral symmetry, and floral color) and five species-level metrics (species strength, weighted closeness, specialization d', nestedness contribution, and modularity contribution) to test how floral traits could directly or indirectly influence species' specialization and network structure in central China. Phylogenetic signals were found in all floral traits except flower density. Structural equation model and phylogenetic structural equation model results showed that both floral size and floral density affected plant specialization and its contribution to network modularity indirectly. However, compared with phylogenetic independent flower density, phylogenetic conserved floral size had much more complexed influences, having a direct influence both on species' specialization and on modularity contribution. In this nested and modular network, abundant species with larger flowers tend to be more central and had larger values of z. Floral shape, symmetry, and color could act as co-flowering filters in pollination sharing and help to shape network modularity. Our results emphasize that phylogenetically conserved traits partially represent pollination syndrome and are important drivers for modular structure of local pollination network. This study may improve the understanding how the evolutionary history and ecological process drive local network structure and dynamics.

Kondo resonance effects in emergent flat band materials

Macroscopic degrees of freedom that are involved in the transport of carriers through mesoscopic electronic devices are susceptible to the effects of strong many-body correlations. The presence of magnetic impurities in dilute magnetic alloys typically allow for insights into Kondo effect from the scattering of free carriers by localized electron states of the magnetic impurities but this effect is not well understood when there are no d-band electron states. Herein, the signatures of Kondo resonance effect are elucidated in quantum dots derived from a carbon-nanoline embedded monolayer hexagonal boron nitride whose electron states host flat band ferromagnetism as distinct broken symmetry states. Quantum transport state of mesoscopic devices modelled as quantum dots tunnel coupled to metallic leads is computed by direct diagonalization of the Hamiltonian. The possibility of realizing quantum dots with highly tunable electron states in energy interconversion devices is discussed to show the importance of screening effects on single-electron energy levels. The quantum master equation is solved within different formalisms to determine the stationary-state particle and energy currents. Stability diagrams are calculated to show the dependence of the conductance on experimental control variables of the quantum dot device. The computed responses of the stationary-state transport signatures are used to characterize Kondo resonance effects from flat band states of embedded carbon nanoline-based quantum dots. It is found that the local network structure of the hexagonal ring carbon cluster-based quantum dot has a broken particle-hole symmetry in the transport state. This signals the formation of the quasiparticle states expected in second order scattering when the macroscopic “charge” pseudospin symmetry of the tunnelling electron state is broken dynamically due to charging. The results are discussed to show the implications of a vanishing particle-hole symmetry in the carrier transport state of quantum dots for energy conversion applications.

Sniffing Technology in the Local Network of the Enterprise Using Wireshark

The work presents the structure and main indicators of the company's local network, means of security analysis and detection of its vulnerability. Threat models are analyzed and risks that may arise when transmitting information on the network are identified. Different means of sniffing are analyzed and their classification is given. On the basis of the conducted analysis and research, recommendations were developed regarding the use of methods and means of combating sniffing in the enterprise's local network. The field of use is cyber security of local networks.

Local Social Network Structure and Promotion Effectiveness in Social Commerce

Local Social Network Structure and Promotion Effectiveness in Social Commerce