Topological Attributes Research Articles

Attribute enhanced random walk for community detection in attributed networks

Traditional community detection methods often rely solely on network topology, potentially overlooking the influence of attributes. However, community detection in attributed networks presents a unique challenge due to the complex interplay between network topology and node attributes. In this paper, we present AERW, a novel approach that integrates both network topology and node attributes for effective community detection. AERW leverages the principle of homophily by aggregating neighbor attributes within triangles to capture higher-order structural information. Then constructing a bipartite graph to effectively integrate topological and attribute information, ensuring that both aspects are equally represented in the community detection process. Next AERW utilizes hierarchical clustering applied to the probability transition matrix derived from the random walk, enabling the identification of community structures without needing prior knowledge of the number of communities. Extensive experimentation across synthetic and real-world networks underscores AERW’s efficacy, establishing it as a superior approach in community detection within attributed networks.

A new Outlook on Omega Closed Functions in Bitopological Spaces and Related Aspects

Many studies have employed a variety of techniques to further investigate topological space, particularly the notion of bitopological spaces, due to the significance of topological space in data processing as well as certain implementations. Numerous extended topological structures have been laid out subsequently. Of those abstractions, functions in topology was one of which was most noteworthy. In order to assist in this trend, we focused our research on the idea of open and closed sets, which is one of the strongest techniques available to present scientists for the study of computer graphics and digital topology.New functions, pairwise ω−closed functions, which are strictly weaker than pairwise closed functions, will be introduced in this study. By applying the P˝−space definition, whose is a P−space modification. Additionally, we establish different projection and product theories pertaining to pairwise Lindelöf of and pairwise paracompact spaces utilizing P˝−spaces. We analyze images and inverse images that have been chosen topological attributes for every one of these functions. In the final analysis, we explore several counterexamples that correspond to the offered definitions and theorems.

TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image

Generative adversarial networks (GANs) have significantly advanced synthetic image generation, yet ensuring topological coherence remains a challenge. This paper introduces TopoSinGAN, a topology-aware extension of the SinGAN framework, designed to enhance the topological accuracy of generated images. TopoSinGAN incorporates a novel, differentiable topology loss function that minimizes terminal node counts along predicted segmentation boundaries, thereby addressing topological anomalies not captured by traditional losses. We evaluate TopoSinGAN using agricultural and dendrological case studies, demonstrating its capability to maintain boundary continuity and reduce undesired loop openness. A novel evaluation metric, Node Topology Clustering (NTC), is proposed to assess topological attributes independently of geometric variations. TopoSinGAN significantly improves topological accuracy, reducing NTC index values from 15.15 to 3.94 for agriculture and 14.55 to 2.44 for dendrology, compared to the baseline SinGAN. Modified FID evaluations also show improved realism, with lower FID scores: 0.1914 for agricultural fields compared to 0.2485 for SinGAN, and 0.0013 versus 0.0014 for dendrology. The topology loss enables end-to-end training with direct topological feedback. This new framework advances the generation of topologically accurate synthetic images, with applications in fields requiring precise structural representations, such as geographic information systems (GIS) and medical imaging.

Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.

Should the multi-layer transportation network structure be reduced?

ABSTRACT The increasing diversity of transportation modes and the rapid expansion of transportation networks present significant challenges for modeling multi-layer comprehensive transportation networks. It is crucial to determine whether aggregating certain layers is a viable option for balancing complexity reduction and information preservation. This decision defines the layered structures and informs subsequent analyses of these networks. Two-dimensional factors, namely topological structures and transportation attributes, are considered to enhance understanding of the similarities among network layers. The relative entropy and the Gini index are employed as metrics to assess information gain or loss resulting from layer aggregation or segregation, guiding decisions on network reduction. Furthermore, an integrated similarity measure, based on the quantum Jensen-Shannon divergence and the Gower distance, is utilized to identify the optimal aggregation sequences. Two real-world transportation networks serve as case studies. Results demonstrate that these transportation networks are more effectively maintained with layer-separated structures, preserving maximum information.

A Non-Linear Control Technique for Single Phase qZSI

Quasi-Z-source inverters, or qZSIs, are a common type of inverter used in transformer-less systems because they can boost or buck input voltage without a requirement for a DC- DC converter. A transformer is not necessary for the qZSI's single-stage conversion to operate as a grid-tie converter. In transformer-less designs common mode current is the major problem, though, because there is no galvanic isolation. To reduce the common mode current, this article offers controlling the qZSI using a modified pulse width modulation (PWM) approach two additional semiconductor switches. The developed methodology provides a practical means of integrating solar photovoltaic systems into the grid. what the recommended topology's attributes should be: 1)It uses the phase-leg shoot through to boost the direct current and voltage to required level by eliminating the need for a second dc-dc converter. 2) Freewheeling is achieved while removing PWM deadtime by employing extra linked switches.

Elastic spin angular momentum of zero-order flexural mode in thin rod system

The exploration of elastic wave behaviors in solid materials has revealed their intricate topological properties in recent years, particularly highlighting the significance of a previously underappreciated characteristic known as elastic spin angular momentum. This aspect plays a crucial role in the dynamics of elastic waves. Our research has focused on elucidating the theoretical aspects of elastic spin angular momentum, especially concerning the zero-order flexural mode in thin rod systems. Through meticulous theoretical analysis, we have deduced the nature of this angular momentum and successfully calculated the distribution of spin within these systems. The implications of our findings are substantial, as they contribute to an enhanced physical comprehension of the topological attributes associated with the flexural mode in thin rod structures, thereby offering new avenues for understanding material behaviors and their potential technological applications.

Star γ Covers and Their Applications to Selection Principles

Abstract By imposing the star operator on γ covers, we establish the notion of st-γ covers. The interrelationships among many recent variants of γ covers have been identified, and various counterexamples have been shown to distinguish them. An investigation has been conducted on the basic topological attributes of these covers. We have also examined the implications between various selection principles and topological games in the context of the class of st-γ coverings.

Topological properties of white matter network in first untreated children and adolescents with obsessive-compulsive disorder

Objective: To investigate the topological properties of the white matter network in the drug-naive first-episode children and adolescent with obsessive-compulsive disorder (OCD). Methods: This study was a case-control study. First-episode OCD childhren and adolescents(OCD group) who were treated in the outpatient or inpatient department of the Second Affiliated Hospital of Xinxiang Medical University from July 2018 to October 2023 were collected as the research subjects. Healthy controls (control group)matched by gender, age and education level were used as controls. Deterministic tractography technique was used to construct the whole brain white matter structural network, and graph theory analysis method was used to analyze the topological attributes of the whole brain white matter structure network in OCD children and adolescents. A network-based statistical method was used to examine the inter-group differences in the functional connectivity strength of the whole brain network. Results: Finally, 31 cases were included in the obsessive-compulsive disorder group, including 22 males and 9 females, with an age of (13.5±1.6) years; There were 34 cases in the control group, including 22 males and 12 females, with an age of (12.7±1.4) years. The global efficiency and local efficiency of the OCD group (0.62±0.03, 0.70±0.07) were significantly higher than those of the control group (0.50±0.06, 0.54±0.21) [both P<0.01, false discovery rate(FDR)correction]; while the characteristic path length (1.77±0.08) was significantly smaller than that of the control group (2.10±0.23) (P<0.01, FDR correction).The centrality comparison of nodal betweenness centrality showed that in the OCD group, the connections were enhanced in the left lateral orbitofrontal gyrus, right dorsal agranular insula, left dorsal granular insula, right granular insula, right posterior central gyrus main area of parietal lobe, left ventral granular insula, granular insula, left ventral granulosa, left granular insula, and left dorsal agranular insula [all P<0.001, family wise error (FWE) correction], while the connection of right thalamic was weakened (P<0.001, FWE correction), There were sub-networks characterized by significantly enhanced connection strength of relevant nodes in subcortical, visual network, and default mode network (P<0.05, permutation test 5 000 times). Conclusions: The topological properties of the brain's functional network in children and adolescents with OCD exhibit abnormalities, indicating an immature state of brain functional connectivity.

Insights into electron and phonon topology: multi-fold fermions and Dirac nodal-line net bosons in SiX2 (X=P and As)

There has been a notable increase in enthusiasm for delving into new (quasi) particles within the domains of condensed matter and materials. Particularly, a distinctive excitement has arisen over a recently theorized exotic excitation found in a three-dimensional material, showcasing linear dispersion and the possibility of multi-fold degeneracy. In this article, we embark on an extensive investigation into the multi-fold degenerate exotic fermions beyond Dirac and Weyl in the pyrite-structured SiX2 compounds (with X as P and As). We delve into the topological attributes of band-touching nodes in fermions, featuring four- and six-fold degeneracy positioned at the Γ and R points in the first Brillouin zone, analyzing them through symmetry analysis. Regarding bosons, employing symmetry analysis, this study identifies distinctive properties where three-nodal surfaces (NSs) and phononic Dirac nodal-line (DNL) nets coexist. These phononic DNL nets comprise three phononic DNLs that share a common nodal point in momentum space, positioned at the intersection of two out of the three k i planes (i = x, y, z). The presence of observable phononic surface states in these materials may facilitate their experimental detection, suggesting these materials’ potential as prime candidates for realizing such phononic states in solid-state materials. Our investigation highlights the pyrite-structured SiX2 as a promising platform for studying multi-fold fermions and delving into their potential applications.

An approach for fault-related monitoring variables selection based on dual-layer correlation networks

PurposeFault-related monitoring variables selection is a process of obtaining a subset of variables from the original set, which is of great significance for reducing information redundancy and improving the performance of the fault diagnosis models. This paper aims to propose a novel variables selection approach based on complex networks.Design/methodology/approachFirstly, a dual-layer correlation networks (DlCN) which consists of mechanism-oriented correlation sub-network (MoCSN) and data-oriented correlation sub-network (DoCSN) is constructed. Secondly, an algorithm for identifying critical fault-related monitoring variables based on dual correlations is introduced. In the algorithm, the topological attributes of the MoCSN and correlation threshold of the DoCSN are used successively.FindingsIn the experiments of vertical elevator fault diagnosis, the critical fault-related monitoring variables selected by the DlCN-based approach is more effective than the traditional approaches. It indicates that fusion mechanism-oriented correlation can enhance the comprehensiveness of variable correlation analysis. Moreover, the approach has been proved to be adaptable to different fault diagnosis models.Originality/valueIn the DlCN-based variables selection approach, the mechanism-oriented correlation and data-oriented correlation are comprehensively considered. It improves the precision of variables selection. Meanwhile, it is an unsupervised and model-agnostic approach which addresses the shortcomings of some conventional approaches that require data labels and have insufficient adaptability for fault diagnosis models.

Graph theoretical analysis of limestone fracture network damage patterns based on uniaxial compression test

The topological attributes of fracture networks in limestone, subject to intense hydrodynamics and intricate geological discontinuities, substantially influence the mechanical and hydraulic characteristics of the rock mass. The dynamical evolution of fracture networks under stress is crucial for unveiling the interaction patterns among fractures. However, existing models are undirected graphs focused on stationary topology, which need optimization to depict fractures' dynamic development and rupture process. To compensate for the time and destruction terms, we propose the damage network model, which defines the physical interpretation of fractures through the ternary motif. We focus primarily on the evolution of node types, topological attributes, and motifs of the fracture network in limestone under uniaxial stress. Observations expose the varying behavior of the nodes' self-dynamics and neighbors' adjacent dynamics in the fracture network. This approach elucidates the impact of micro-crack behaviors on large brittle shear fractures from a topological perspective and further subdivides the progressive failure stage into four distinct phases (isolated crack growth phase, crack splay phase, damage coalescence phase, and mechanical failure phase) based on the significance profile of the motif. Regression analysis reveals a positive linear and negative power correlation between fracture network density and branch number to the rock damage resistance, respectively. The damage network model introduces a novel methodology for depicting the interaction of two-dimensional (2D) projected fractures, considering the dynamic spatiotemporal development characteristics and fracture geometric variation. It helps dynamically characterize properties such as connectivity, permeability, and damage factors while comprehensively assessing damage in rock mass fracture networks.

Machine learning methods for unveiling the potential of antioxidant short peptides in goat milk-derived proteins during in vitro gastrointestinal digestion

Milk serves as an important dietary source of bioactive peptides, offering notable benefits to individuals. Among the antioxidant short peptides (di- and tripeptides) generated from gastrointestinal digestion are characterized by enhanced bioavailability and bioaccessibility, while assessing them individually presents a labor-intensive and expensive challenge. Based on 4 distinct types of AA descriptors (physicochemical, 3-dimensional structural, quantum, and topological attributes) and genetic algorithms for feature selection, 1 and 4 machine learning-predicted models separately for di- and tripeptides with 2,2-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical scavenging capacity exhibited excellent fitting and prediction ability with random forest regression as machine learning algorithm. Intriguingly, the electronic properties of N-terminal AA were considered as only factor affecting the antioxidant capacity of dipeptides containing both tyrosine and tryptophan. Four peptides from the potential di- and tripeptides exhibited highly predicted values by the constructed predicted models. Subsequently, a total of 45 dipeptides and 52 tripeptides were screened by a customized workflow in goat milk during in vitro simulated digestion. In addition to 5 known antioxidant dipeptides, 9 peptides were quantified during digestion, exhibiting concentrations ranging from 0.04 to 1.78 mg L-1. Particularly noteworthy was the promising in vivo functionality of antioxidant dipeptides with N-terminal tyrosine, supported by in silico assays. Overall, this investigation explored crucial molecular properties influencing antioxidant short peptides and high-throughput screening potential peptides with antioxidant activity from goat milk aided by machine learning, thereby facilitating the discovery of novel functional peptides from milk-derived proteins and paving the way for understanding their metabolites during digestion.

A demand-based three-stage seismic resilience assessment and multi-objective optimization method of community water distribution networks

This paper proposes a three-stage resilience concept that takes into account reliability, vulnerability and recovery resilience of water distribution networks (WDNs) from three phases, namely, pre-disaster preparation (PDP) phase, disaster response (DR) phase and the post-disaster recovery (PDR) phase. Based on the three-stage resilience concept, the initial performance, robustness and multi-objective optimization recovery method are proposed. In PDP phase, based on the reliability concept and the user demand, the initial performance of WDNs is defined, and the method is proposed based on Monte Carlo simulation method combined with seismic fragility of pipelines. In DR phase, a comprehensive quantitative index is proposed to quantify the robustness of WDNs by integrating the structural attributes, topological attributes and operational characteristics of WDNs. Finally, on the basis of the previous two stages, the disaster recovery process of failed pipelines of WDNs is further analyzed, and a multi-objective recovery optimization model of failed pipelines with consideration of users’ demands under the resources constraints is proposed to achieve the resilience enhancement. Through a case, the three-stage resilience assessment optimization enhancement of WDNs is implemented using the improved NSGA-II. The proposed method can be used as a basis for disaster prevention planning and seismic measures.

Emergent dynamics: Collective motions of polar active particles on surfaces

In this study, we focus on the collective dynamics of polar active particles navigating across three distinct surfaces, each characterized by its own unique blend of topological and geometrical properties. The behavior of these active particles is influenced by a multitude of factors, including self-propulsion, inter-particle interactions, surface constraints, and under-damped stochastic forces simulated via Ornstein–Uhlenbeck processes. Our exploration unveils the prevailing collective patterns observed within these systems across three surface types: a sphere, a torus, and a landscape featuring hills and valleys, each distinguished by its specific topological and geometrical attributes. We underscore the profound impact of surface curvature and symmetry on the sustainable spatial-temporal dynamics witnessed. Our findings illuminate how the interplay between substantial surface curvature and particular symmetrical characteristics gives rise to a diverse spectrum of spatial-temporal patterns. Notably, we discern that high curvature tends to drive collective motion toward cyclic rotation on spheres and tori, or spatial-temporal periodic traveling ring patterns on landscapes with hills and valleys. Additionally, we observe that rough surfaces and the incorporation of excluded volume effects can disrupt the complexity of these collective spatial-temporal patterns. Through this investigation, we provide invaluable insight into the intricate interplay of curvature and symmetry, profoundly shaping collective behaviors among active particles across varied surfaces.

Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis

AbstractThe visual analysis of multivariate network data is a common yet difficult task in many domains. The major challenge is to visualize the network's topology and additional attributes for entities and their connections. Although node‐link diagrams and adjacency matrices are widespread, they have inherent limitations. Node‐link diagrams struggle to scale effectively, while adjacency matrices can fail to represent network topologies clearly. In this paper, we delve into the design space of BioFabric, which aligns entities along rows and relationships along columns, providing a way to encapsulate multiple attributes for both. We explore how we can leverage the unique opportunities offered by BioFabric's design space to visualize multivariate network data — focusing on three main categories: juxtaposed visualizations, embedded on‐node and on‐edge encoding, and transformed node and edge encoding. We complement our exploration with a quantitative assessment comparing BioFabric to adjacency matrices. We postulate that the expansive design possibilities introduced in BioFabric network visualization have the potential for the visualization of multivariate data, and we advocate for further evaluation of the associated design space. Our supplemental material is available on osf.io.

Spatial circulatory networks based on the peripheral medial axis transform

This article presents a method for constructing circulatory networks that intrinsically combine geometric, topological, and semantic spatial attributes. The objective of this work is to generate concise graph representations extracted from spatial geometry that may be used for wayfinding and code compliance analyses to inform architectural design and urban planning. Our approach is based on shape analysis using the Medial Axis Transform skeletonization method. We present a process for the semantic classification of its nodes and edges by constructing an analytical rational piece-wise 3D surface representation. This overcomes the problem of identifying salient or otherwise graph features. Furthermore, we augment the networks with peripheral pathways. This addresses the fundamental limitation of skeletal graphs forcing paths exclusively through the middle of space and thus producing unreasonable detours. We demonstrate that our approach encompasses subtle circulatory features and generates concise graphs for open and free-form spaces that do not exhibit corridor-like structures.

Overlapping complexes detection within protein interaction networks using improved genetic algorithm

BackgroundThe protein complexes detection has an important role in analyzing biological processes and understanding life at the cellular level. Although many complex detection algorithms have been proposed by employing specific topological attributes of protein interaction networks, most of them focus on detecting separated protein complexes, where each protein can belong to only one complex. Generally, many protein interaction networks have many complexes or modules that are often overlapped with each other. Developing overlapping protein complex detection algorithm thus becomes necessary. MethodsIn this respect, this paper proposes a new overlapping detection algorithm based on GA (Genetic Algorithm) to discover overlapping protein complexes. The proposed algorithm is supplied with a new local search operator based on gene ontology annotations in order to direct the search process towards discovering overlapping protein complexes that are hyper-connected and biologically related. The proposed algorithm is assessed on two gold standard and real-world datasets. ResultsThe result attained shows that the proposed algorithm can detect important overlapping protein complexes, and presents more accurate results compared to the state-of-the-art overlapping protein complexes detection algorithms. ConclusionsThe superior performance of the proposed algorithm compared with other counterpart algorithms is attributed to the fact that the proposed algorithm is a population based stochastic search algorithm which efficiently explored the search landscape to capture dense overlapping subgraphs by employing the topological properties of network, and then enhancing the obtained solution by investing gene ontology information in developing a new local search operator inside the GA framework.

Modification and completion of geological structure knowledge graph based on pattern matching

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out.

A Review on Currency Classification and Image to Text Conversion Methodologies

Currency classification and Image to Text OCR are essential technologies that find applications in various domains, including finance, retail, and automation. The approach outlined in this paper has the potential to detect currencies from multiple countries. However, for practical implementation purposes, the focus is solely on Indian paper currencies. This system offers the advantage of convenient currency checking at any time and location, leveraging Convolutional Neural Networks (CNN) for effective implementation. Extensive testing was conducted on each denomination of Indian currency, resulting in an impressive 95% accuracy rate. To further refine accuracy, a classification model was developed, incorporating all pertinent factors discussed in the paper. Notably, the unique features of paper currency play a pivotal role in the recognition process. By emphasizing these elements and harnessing CNN technology, the proposed system demonstrates significant promise in accurately detecting and validating Indian paper currencies. It stands poised to serve various applications effectively. On the other hand, Image to Text OCR focuses on extracting text from images, enabling the conversion of non- editable documents into searchable and editable formats. Both technologies contribute to automation and efficiency in handling diverse visual information. Optical Character Recognition (OCR) is a technologydesigned to recognize and interpret both printed and handwritten characters by scanning text images. This process involves segmenting the text image into regions, isolating individual lines, and identifying each character along with its spacing. After isolating individual characters from the text image, the system conducts an analysis of their texture and topological attributes. This involves examining corner points, unique characteristics of various regions within the characters, and calculating the ratio of character area to convex area Prior to initiating recognition, the system creates templates that store the distinctive features of uppercase and lowercase letters, digits, and symbols. These templates serve as reference models for comparison during the recognition phase. During recognition, the system matches the extracted character's texture and topological Features with those stored in the templates to determine the exact character. This matching process involves comparing features of the extracted character with templates of all characters, measuring similarity, and ultimately recognizing the character accurately.