Local Network Structure Research Articles

A novel deep neural network-based technique for network embedding

In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the underlying local network topology. The key innovation of GSeg lies in its encoder-decoder architecture, which is specifically designed to preserve the network’s structural properties. The key contributions of GSeg are: (1) a novel graph neural network architecture that effectively captures local and global network structures, and (2) a robust node representation learning approach that achieves superior performance in various network analysis tasks. The methodology employed in our study involves the utilization of a graph neural network framework for the acquisition of node representations. The design leverages the inherent characteristics of nodes and the underlying local network topology. To enhance the architectural framework of encoder- decoder networks, the GSeg model is specifically devised to exhibit a structural resemblance to the SegNet model. The obtained empirical results on multiple benchmark datasets demonstrate that the GSeg outperforms existing state-of-the-art methods in terms of network structure preservation and prediction accuracy for downstream tasks. The proposed technique has potential utility across a range of practical applications in the real world.

Alleviating the resolution limit problem in spatial community detection: a local network structure-based method

Spatial community detection plays a crucial role in the analysis of spatially embedded networks. However, most existing methods adopt modularity as an objective function, which may fail to detect small spatial communities (i.e. the well-known resolution-limit problem). To alleviate this problem, a local network structure-based spatially constrained Leiden method was developed. First, the weights of the edges were reset based on the local network structure, which facilitated a clearer delineation between distinct communities. Second, we extended Leiden, an effective community detection method, to spatial community detection content by adding spatial constraints. Experiments on simulated datasets demonstrated that the proposed method is superior to four state-of-the-art methods for detecting spatial communities of different sizes. A case study conducted using the Shenzhen taxi dataset demonstrated that the proposed method outperformed four state-of-the-art methods in revealing urban spatial structures. Notably, the modularity of the spatial communities detected using the proposed method exhibited a marked improvement, nearly doubling that of the four comparative methods in the case study. This study presents a novel and promising framework for detecting spatial communities using modularity.

Maintenance of delay-period activity in working memory task is modulated by local network structure.

Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.

Modelling network motifs as higher order interactions: a statistical inference based approach

The prevalent approach to motif analysis seeks to describe the local connectivity structure of networks by identifying subgraph patterns that appear significantly more often in a network then expected under a null model that conserves certain features of the original network. In this article we advocate for an alternative approach based on statistical inference of generative models where nodes are connected not only by edges but also copies of higher order subgraphs. These models naturally lead to the consideration of latent states that correspond to decompositions of networks into higher order interactions in the form of subgraphs that can have the topology of any simply connected motif. Being based on principles of parsimony the method can infer concise sets of motifs from within thousands of candidates allowing for consistent detection of larger motifs. The inferential approach yields not only a set of statistically significant higher order motifs but also an explicit decomposition of the network into these motifs, which opens new possibilities for the systematic study of the topological and dynamical implications of higher order connectivity structures in networks. After briefly reviewing core concepts and methods, we provide example applications to empirical data sets and discuss how the inferential approach addresses current problems in motif analysis and explore how concepts and methods common to motif analysis translate to the inferential framework.

The role of network structure and complexity for green inventions

ABSTRACT Several studies underline that the production of green inventions might require different competences and resources. Yet, as far as we are aware, few studies assess the role of networks for green inventions considering the presence of a diversified set of competences at the local level. This study investigates the differentiated role of local network structure and complexity in producing green and non-green inventions in Italy. Results underscore that the role of the local intra-province network, rather than exhibiting a differentiated effect for green and non-green inventions, yielded similar (positive) outcomes. Besides, external inter-province collaborations and economic complexity, on the other hand, demonstrated divergent effects, with the former showing a stronger influence on green inventions and the latter serving as a significant driver exclusively for the production of green innovations.

Multi-Channel Multi-Scale Convolution Attention Variational Autoencoder (MCA-VAE): An Interpretable Anomaly Detection Algorithm Based on Variational Autoencoder.

With the rapid development of industry, the risks factories face are increasing. Therefore, the anomaly detection algorithms deployed in factories need to have high accuracy, and they need to be able to promptly discover and locate the specific equipment causing the anomaly to restore the regular operation of the abnormal equipment. However, the neural network models currently deployed in factories cannot effectively capture both temporal features within dimensions and relationship features between dimensions; some algorithms that consider both types of features lack interpretability. Therefore, we propose a high-precision, interpretable anomaly detection algorithm based on variational autoencoder (VAE). We use a multi-scale local weight-sharing convolutional neural network structure to fully extract the temporal features within each dimension of the multi-dimensional time series. Then, we model the features from various aspects through multiple attention heads, extracting the relationship features between dimensions. We map the attention output results to the latent space distribution of the VAE and propose an optimization method to improve the reconstruction performance of the VAE, detecting anomalies through reconstruction errors. Regarding anomaly interpretability, we utilize the VAE probability distribution characteristics, decompose the obtained joint probability density into conditional probabilities on each dimension, and calculate the anomaly score, which provides helpful value for technicians. Experimental results show that our algorithm performed best in terms of F1 score and AUC value. The AUC value for anomaly detection is 0.982, and the F1 score is 0.905, which is 4% higher than the best-performing baseline algorithm, Transformer with a Discriminator for Anomaly Detection (TDAD). It also provides accurate anomaly interpretation capability.

The driving mechanisms of industrial air pollution spatial correlation networks: A case study of 168 Chinese cities

To enhance regional collaborative management and effectively address air pollution, this paper constructs the industrial air pollution network of 168 cities in China between 2010 and 2021. Social network analysis (SNA) and temporal exponential random graph model (TERGM) are used to investigate the structural characteristics and driving mechanisms of industrial air pollution spatial correlation networks. The results indicate the following: (1) Industrial air pollution levels in key Chinese cities showed a slight fluctuation between 2010 and 2015, but decreased after 2015. (2) The industrial air pollution linkage network exhibits small-world characteristics and path dependence, with different cities playing distinct roles in the network. (3) The evolution of the network is influenced by both exogenous and endogenous mechanisms. Local network structures such as reciprocity, connectivity, and transitivity make the network exhibit path-dependent characteristics. Cities’ attributes such as economic development level, green innovation capacity, and population density significantly influenced their status in the network and linkages between cities. Pollution linkages are more likely to occur in cities with similar levels of economic development, green technological innovation capacity, and institutional environments. Therefore, effective regional division for scientific collaborative governance should be considered from multiple perspectives. Based on these results, the paper provides several policy recommendations.

BioPipeline Creator—a user-friendly Java-based GUI for managing and customizing biological data pipelines

Bioinformatics tools are essential for performing analyses in the omics sciences. Given the numerous experimental opportunities arising from advances in the field of omics and easier access to high-throughput sequencing platforms, these tools play a fundamental role in research projects. Despite the considerable progress made possible by the development of bioinformatics tools, some tools are tailored to specific analytical goals, leading to challenges for non-bioinformaticians who need to integrate the results of these specific tools into a customized pipeline. To solve this problem, we have developed the BioPipeline Creator, a user-friendly Java-based GUI that allows different software tools to be integrated into the repertoire while ensuring easy user interaction via an accessible graphical interface. Consisting of client and server software components, BioPipeline Creator provides an intuitive graphical interface that simplifies the use of various bioinformatics tools for users without advanced computer skills. It can run on less sophisticated devices or workstations, allowing users to keep their operating system without having to switch to another compatible system. The server is responsible for the processing tasks and can perform the analysis in the user's local or remote network structure. Compatible with the most important operating systems, available at https://github.com/allanverasce/bpc.git.

Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning

BackgroundCompared with the time-consuming and labor-intensive for biological validation in vitro or in vivo, the computational models can provide high-quality and purposeful candidates in an instant. Existing computational models face limitations in effectively utilizing sparse local structural information for accurate predictions in circRNA-disease associations. This study addresses this challenge with a proposed method, CDA-DGRL (Prediction of CircRNA-Disease Association based on Double-line Graph Representation Learning), which employs a deep learning framework leveraging graph networks and a dual-line representation model integrating graph node features.MethodCDA-DGRL comprises several key steps: initially, the integration of diverse biological information to compute integrated similarities among circRNAs and diseases, leading to the construction of a heterogeneous network specific to circRNA-disease associations. Subsequently, circRNA and disease node features are derived using sparse autoencoders. Thirdly, a graph convolutional neural network is employed to capture the local graph network structure by inputting the circRNA-disease heterogeneous network alongside node features. Fourthly, the utilization of node2vec facilitates depth-first sampling of the circRNA-disease heterogeneous network to grasp the global graph network structure, addressing issues associated with sparse raw data. Finally, the fusion of local and global graph network structures is inputted into an extra trees classifier to identify potential circRNA-disease associations.ResultsThe results, obtained through a rigorous five-fold cross-validation on the circR2Disease dataset, demonstrate the superiority of CDA-DGRL with an AUC value of 0.9866 and an AUPR value of 0.9897 compared to existing state-of-the-art models. Notably, the hyper-random tree classifier employed in this model outperforms other machine learning classifiers.ConclusionThus, CDA-DGRL stands as a promising methodology for reliably identifying circRNA-disease associations, offering potential avenues to alleviate the necessity for extensive traditional biological experiments. The source code and data for this study are available at https://github.com/zywait/CDA-DGRL.

RELATIONAL SEDIMENTS IN THE BACKWASH OF PROTEST WAVES: EXPLORING NETWORK CONSEQUENCES OF COLLECTIVE ACTION

This article addresses the puzzling relational void in the analysis of collective action consequences by exploring how protest waves affect local network structures in two German towns. We compare three periods of time: a preprotest wave latency period, a protest wave period, and a postwave latency period, as defined in relation to contentious collective action around the refugee reception crisis in 2015. Adopting a field perspective, our analysis uses digital communication data to capture the referencing practices between all actors involved in local migration-related protests irrespective of actor type or political orientation. This results in networks of both supportive and antagonistic interaction. Applying network analysis, especially multiple regression quadratic assignment procedures (MRQAP), we find significant and distinct patterns of change and stability for supportive and antagonistic ties. We argue that these findings provide stepping stones for the emergent literature on relational consequences of collective action.

Rail Transit Networks and Network Motifs: A Review and Research Agenda

The railway plays an essential role in urban and intercity transport of goods and people. Intercity and urban rail transit infrastructures contribute to the economic and environmental sustainability of global economies. Those infrastructures can be modeled as complex networks, so that we can evaluate system properties of the network structure. This stream of research has focused on the topological analysis of global network structure, but little research exists that examines how local network structures affect system properties. The local structure of complex networks can be examined with network motif analysis, as those network motifs are the building blocks of networked systems. Nevertheless, there has been scarce attention given to local network properties in rail transit networks. We contribute to covering this gap in the literature with a literature review of motif analysis research and its application to weighted and unweighted rail transit networks, also covering the current state-of-the-art of network motif decomposition and analysis. We demonstrate that network motif analysis is not only applicable, but also beneficial for the design and planning of rail transit networks, enhancing their sustainability by improving efficiency, reducing environmental impact, and optimizing resource allocation. Based on our findings, we propose future research directions that involve applying motif analysis to enhance the sustainability features of both unweighted and weighted rail transit networks.

Scaling theory of fractal complex networks

We show that fractality in complex networks arises from the geometric self-similarity of their built-in hierarchical community-like structure, which is mathematically described by the scale-invariant equation for the masses of the boxes with which we cover the network when determining its box dimension. This approach—grounded in both scaling theory of phase transitions and renormalization group theory—leads to the consistent scaling theory of fractal complex networks, which complements the collection of scaling exponents with several new ones and reveals various relationships between them. We propose the introduction of two classes of exponents: microscopic and macroscopic, characterizing the local structure of fractal complex networks and their global properties, respectively. Interestingly, exponents from both classes are related to each other and only a few of them (three out of seven) are independent, thus bridging the local self-similarity and global scale-invariance in fractal networks. We successfully verify our findings in real networks situated in various fields (information—the World Wide Web, biological—the human brain, and social—scientific collaboration networks) and in several fractal network models.

Node2Vec and Machine Learning: A Powerful Duo for Link Prediction in Social Network

Link prediction in social networks is a challenging task that attempts to uncover hidden linkages and forecast future connections. The link prediction problem is addressed in this research article by utilizing the capabilities of Node2Vec and machine learning algorithms. To learn high-dimensional node representations that capture both local and global network structures, the Node2Vec technique is used. Then, in order to forecast potential connections, these node embedding’s are put into various machine learning models. Two real-world social network datasets are used to test the suggested methodology, and the findings show a considerable improvement in link prediction accuracy. It achieves a deeper comprehension of the hidden relationships in social networks by fusing the semantic richness of Node2Vec embedding’s with the predictive powers of machine learning methods. The results of this study extend link prediction approaches in social networks by revealing hidden ties and providing insightful predictions for upcoming connections. The suggested method indicates the potential for real-world applications in a number of fields, including recommender systems, targeted advertising, and social influence studies.

Amorphous carbon nitride (C3N4)

This detailed investigation employs an ab initio approach to explore the atomic structure and electronic properties of an amorphous carbon nitride (C3N4) model. The model, designed with an exact 3:4 ratio, is based on an amorphous boron nitride configuration. The study reveals crucial insights into the mean coordination number for C and N atoms within the amorphous structure. With values of 2.95 for C atoms and 2.21 for N atoms, these coordination numbers closely resemble those observed in graphite-like crystals. The local structure of the amorphous network exhibits similarities to the triazine-based graphitic C3N4 crystal and is notably devoid of homopolar bonds. The estimated band gap for the amorphous C3N4 model is 1.2 eV, representing a significant reduction compared to the crystal structure, which exhibits a band gap of about 2.93 eV as determined through GGA+U calculations.

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks.

A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor's integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network's local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra's Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra's URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.

AutoTarget: Disease-Associated druggable target identification via node representation learning in PPI networks

Drug target discovery, a pivotal early stage in drug development, is resource-intensive and crucial for ensuring drug efficacy. This study presents AutoTarget, a novel computational pipeline designed to identify disease-associated druggable targets by applying node representation learning to protein–protein interaction (PPI) networks. AutoTarget uses node2vec + for node classification, incorporating neighborhood context and structural equivalence in PPI networks derived from the STRING database. Data from the Therapeutic Target Database (TTD) and DisGeNET were integrated to identify known drug targets and gene-disease associations, respectively. Each protein is embedded into a 128-dimensional vector space, capturing local network structures and enabling the identification of structurally equivalent proteins. A Naïve Bayes classifier, trained on these embeddings, achieved a recall of 0.90 and an F1 score of 0.79 in predicting potential drug targets. AutoTarget identified 3,979 novel potential druggable target proteins out of 19,333 proteins in the PPI network, which were mapped to 23,363 diseases using DisGeNET. This creates a comprehensive resource for disease-specific drug target exploration. Case studies on triple-negative breast cancer and obesity demonstrated AutoTarget’s capability to identify both established and emerging targets, such as CD44, MAPK3, and GIP. Visualization of embedding vectors using t-SNE revealed clear separations between functional protein families, including nuclear proteins, growth factor receptors, and the G proteins within the kinase proteins. This supports the method’s ability to capture biologically relevant information. However, limitations were noted, including the inability to distinguish between different types of disease-associated proteins based solely on network features. Overall, this study advances the application of machine learning and network theory for identifying druggable targets across a wide range of diseases. AutoTarget provides researchers with a valuable tool for expediting the discovery of novel druggable targets, potentially streamlining the drug discovery process. The AutoTarget code and database are publicly available to facilitate further research.

Central limit theorems for local network statistics

Summary Subgraph counts, in particular the number of occurrences of small shapes such as triangles, characterize properties of random networks. As a result, they have seen wide use as network summary statistics. Subgraphs are typically counted globally, making existing approaches unable to describe vertex-specific characteristics. In contrast, rooted subgraphs focus on vertex neighbourhoods, and are fundamental descriptors of local network properties. We derive the asymptotic joint distribution of rooted subgraph counts in inhomogeneous random graphs, a model that generalizes most statistical network models. This result enables a shift in the statistical analysis of graphs, from estimating network summaries to estimating models linking local network structure and vertex-specific covariates. As an example, we consider a school friendship network and show that gender and race are significant predictors of local friendship patterns.

Concentration dependent cyan emission by europium‐doped borosilicate glasses

AbstractAt present, doping of different alkaline‐earth metal ions is mainly used to tune the emission peak of europium (Eu) ions in glasses. However, the optimization of Eu‐doped glasses is hindered due to limited discussion on the relationship between local glass network structure, concentration of Eu ion dopants, and visible light emission by Eu ions. In this study, we prepared two series of borosilicate glasses doped with Eu ions, by varying the ratio of SiO2 to B2O3 and adjusting the concentration of Eu ion dopants to obtain cyan emission. The optical properties, structure, and differential scanning calorimetry (DSC) of the investigated glasses were systematically studied. With gradual increase in the content of SiO2, red shifting from 471 nm to 492 nm was observed under excitation at 410 nm, with the introduction of Eu2O3 also causing a redshift from 471 nm to 488 nm. The spectral results also revealed that under excitation at 410 nm all the glass samples exhibited cyan emission, with the optimal cyan peak at 492 nm (Si‐45) and the strongest cyan emission at Eu‐0.4 (480 nm). By adjusting the concentration ratios of Si to B in europium‐doped borosilicate glass, it was observed that by altering the [SiO4] tetrahedra, [BO4] tetrahedra, and [BO3] triangles within the glass ultimately cyan emission can be achieved. By varying the doping concentration of Eu ion to change the degree of local Eu ion clustering in the glass system, cyan emission can be achieved by tuning the emission peak.

Link prediction in bipartite networks via effective integration of explicit and implicit relations

Link prediction in bipartite networks aims to identify or predict possible links between nodes of different types based on known network information. However, most existing studies predominantly focus on monopartite networks, neglecting the intrinsic properties unique to bipartite networks, such as the intricate high-order relationships between nodes. Both explicit relations (representing low-order information) and implicit relations (representing high-order information) play essential roles in predicting the evolution of bipartite networks, and they are indispensable and mutually reinforce each other. To fully leverage their potential in addressing the link prediction problem, we propose a novel framework from the perspective of network representation. This framework not only effectively integrates explicit relations and implicit relations, but also preserves the local and global structure of bipartite networks. Specifically, the probability of a link between two nodes of different types is determined by the linear sum of the contribution values of the mutually connected nodes in their respective common neighbors. Implicit relations are then used to preserve the local structure during the network representation. Furthermore, we implement optimization using a relaxed majorization-minimization algorithm, offering the advantage of uncovering high-quality local minima. Our proposed framework has undergone extensive testing on eight real-world datasets, and the results unequivocally demonstrate its significant superiority over state-of-the-art methods.

Modular tipping points: How local network structure impacts critical transitions in networked spin systems

Critical transitions describe a phenomenon where a system abruptly shifts from one stable state to an alternative, often detrimental, stable state. Understanding and possibly preventing the occurrence of a critical transition is thus highly relevant to many ecological, sociological, and physical systems. In this context, it has been shown that the underlying network structure of a system heavily impacts the transition behavior of that system. In this paper, we study a crucial but often overlooked aspect in critical transitions: the modularity of the system’s underlying network topology. In particular, we investigate how the transition behavior of a networked system changes as we alter the local network structure of the system through controlled changes of the degree assortativity. We observe that systems with high modularity undergo cascading transitions, while systems with low modularity undergo more unified transitions. We also observe that networked systems that consist of nodes with varying degrees of connectivity tend to transition earlier in response to changes in a control parameter than one would anticipate based solely on the average degree of that network. However, in rare cases, such as when there is both low modularity and high degree disassortativity, the transition behavior aligns with what we would expected given the network’s average degree. Results are confirmed for a diverse set of degree distributions including stylized two-degree networks, uniform, Poisson, and power-law degree distributions. On the basis of these results, we argue that to understand critical transitions in networked systems, they must be understood in terms of individual system components and their roles within the network structure.