Node Connectivity Research Articles

Context propagation based influence maximization model for dynamic link prediction

Influence maximization (IM) in dynamic social networks is an optimization problem to analyze the changes in social networks for different periods. However, the existing IM methods ignore the context propagation of interaction behaviors among users. Hence, context-based IM in multiplex networks is proposed here. Initially, multiplex networks along with their contextual data are taken as input. Community detection is performed for the network using the Wilcoxon Hypothesized K-Means (WH-KMA) algorithm. From the detected communities, the homogeneous network is used for extracting network topological features, and the heterogeneous networks are used for influence path analysis based on which the node connections are weighted. Then, the influence-path-based features along with contextual features are extracted. These extracted features are given for the link prediction model using the Parametric Probability Theory-based Long Short-Term Memory (PPT-LSTM) model. Finally, from the network graph, the most influencing nodes are identified using the Linear Scaling based Clique (LS-Clique) detection algorithm. The experimental outcomes reveal that the proposed model achieves an enhanced performance.

Which comes first? Comorbidity of depression and anxiety symptoms: A cross-lagged network analysis

Depression and anxiety significantly impact college students, leading to various negative outcomes. While numerous studies have investigated the relationship between these two conditions, their temporal sequence remains unresolved. Many previous studies have concentrated on broad latent variables, often neglecting the nuanced symptomatology perspective, which may offer deeper insight into the clinical characteristics of these disorders. In this study, we collected questionnaire data from a college in South China using a cluster random sampling method. Data collection occurred over two time points, with the first round completed in November 2022 and May 2023, with a six-month interval. A total of 689 participants successfully completed the questionnaires during both rounds. Employing cross-lagged network analysis from a symptom-focused perspective, this research examines the interactions and predictive relationships between symptoms of depression and anxiety. The findings identified key symptoms-specifically “Irritability”, “Guilty” and “Sad mood"- as critical bridging nodes of connection within the depression and anxiety symptom network. Our analysis revealed both bidirectional predictive relationships between certain symptoms nodes of depression and anxiety, as well as unidirectional ones. By highlighting these core nodes and their directional relationships, this study offers valuable insights that can inform targeted intervention and treatment strategies for enhancing mental health among college students.

Experimental study on eccentric compression of the prefabricated columns with a new type of column-column connection node

An assembled building is defined by a construction methodology wherein prefabricated components are fabricated off-site and subsequently assembled at the construction site. This methodology confers cost efficiency, expedites construction timelines, and enhances the quality of the prefabricated components. Furthermore, it mitigates a range of environmental concerns inherent to conventional poured-in-place construction methods. This paper introduces a novel assembled column-column connection node that utilizes a new adhesive potted rebar lap joint. The research was divided into two principal components. Firstly, 18 specimens of adhesive potted rebar lap joints were tested under tension, with the lap length being the variable parameter. Secondly, eccentric compression experiments were conducted on one poured-in-place column and six assembled columns, varying parameters such as stirrup configuration and the length of the post-poured section. The failure modes of the columns, as well as their bearing capacities, deflection, rebar strains, and concrete strains, were analyzed and recorded. The test results revealed the existence of two different failure modes in the tensile tests of adhesive potted rebar lap joints. It was found that the reasonable lap length of the specimen should be no less than 14 times the diameter of the rebar (d). Eq. (15) was employed to estimate the suitable lap length for specimens with different sleeve and rebar diameters. In the failure sections of the prefabricated columns, all failures occurred outside the nodes. Moreover, the mechanical properties of small eccentrically compressed columns were comparable to those of the poured-in-place column. The use of spiral stirrups enhances the bearing capacity of the specimen, and an increase in the length of the post-poured section corresponds to a greater bearing capacity. The lateral deflection of the poured-in-place column generally follows a sinusoidal curve, while the lateral deflection of prefabricated columns in the later stages of loading does not exhibit a sinusoidal pattern. Both align with the assumption of a flat section. The stress performance of adhesive potted rebar lap joints in eccentric columns meets the strength requirements. The inclusion of tenons minimally affects the stress distribution in the node, fulfilling construction specifications. The stress performance of the assembled column nodes is reliable. The proposed adhesive potted rebar lap joint technology, along with the novel prefabricated column-column node, provides significant insights into engineering design and construction applications.

Vulnerability assessment and evolution analysis of Beijing's Urban Rail Transit Network

With the development of urban rail transit system, the complexity of the network intensifies, and its vulnerability shifts correspondingly. Understanding the characteristics and evolution of network vulnerability, as well as identifying developmental patterns, enables more scientific network planning. Current researches on network vulnerability predominantly focus on the static vulnerability assessment of existing networks, with limited studies on vulnerability evolution. This paper divides the topological evolution of the Beijing Urban Rail Transit Network (BURTN) from 2000 to 2020 into Formation and Improvement stages using the K-means++ method. By constructing a multidimensional vulnerability assessment model that considers node degree uniformity, network efficiency, and connectivity, the vulnerability evolution characteristics and patterns of BURTN are explored in cases of both Single-station failures and Multi-station consecutive failures (including random and intentional failures). Furthermore, the evolutionary relationship between network vulnerability and network structure is explored using the Ridge regression model. Calculations reveal that in the case of Single-station failures, during the Formation stage, the proportion of highly vulnerable stations (HVS) and the impact of each station failure on network performance decrease significantly, by 69.36 % and 67.67 %, respectively. During the Improvement stage, the proportion of HVS decreases significantly, while the impact of each station failure on network performance decreases slightly, by 79.10 % and 37.04 %, respectively. In the case of Multi-station consecutive failures, during the Formation stage, the network’s ability to cope with both random and intentional failures decreases, with the percentage of network nodes removed at the collapse state decreasing by 24.95 % and 11.12 %, respectively. During the Improvement stage, the network’s ability to cope with random failures remains stable, while its ability to cope with intentional failures decreases. This study helps to understand vulnerability from an evolutionary perspective and provides practical strategies for reducing vulnerability.

Feature-aware ultra-low dimensional reduction of real networks

In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. This study introduces FiD-Mercator, a model-based ultra-low dimensional reduction technique that integrates node features with network structure to create D-dimensional maps of complex networks in a hyperbolic space. This embedding method efficiently uses features as an initial condition, guiding the search of nodes’ coordinates toward an optimal solution. The research reveals that downstream task performance improves with the correlation between network connectivity and features, emphasizing the importance of such correlation for enhancing the description and predictability of real networks. Simultaneously, hyperbolic embedding’s ability to reproduce local network properties remains unaffected by the inclusion of features. The findings highlight the necessity for developing network embedding techniques capable of exploiting such correlations to optimize both network structure and feature association jointly in the future.

Optimisation of Parameters and Application of Green Recyclable Precast Hollow Steel Pipe Concrete Supports

Underground space development is a crucial approach to addressing traffic congestion in China’s cities, especially through underground construction. However, the traditional pit internal support system, particularly the concrete internal support system, has significant drawbacks. These include high energy consumption and carbon emissions, which are increasingly prominent issues. In this paper, a hollow precast concrete-filled steel tube (H-CFST) internal support system is proposed and a node connection scheme is designed. Through numerical simulation, the load-bearing characteristics of H-CFST with different aspect ratios, hollow ratios, hoop thicknesses, and hoop lengths are investigated, and the optimal design parameters are obtained. Finally, following a field application, monitoring data reveal that the precast H-CFST internal support demonstrates superior load-bearing capacity compared to the concrete internal support, successfully meeting the criteria for replacement of the concrete support.

Enhancing ecological connectivity in the Qilian Mountains: Integrating GCA and optimized MST models for ecological corridor construction

Amidst global climate change and increased anthropogenic activities, the Qilian Mountains face critical ecological and environmental challenges, including vegetation degradation and declining carbon stocks. Therefore, effective mitigation strategies are critical to building China’s ecological security and enhancing carbon storage. This study constructed and optimized an ecological corridor in the Qilian Mountains using models, such as minimum spanning tree and carbon stock calculation. It combined geographic meta cellular automata and other methods to model ecological recovery in the Qilian Mountains following corridor construction and investigated the areas of particular concern. The results show that: (1) the distribution of the 118 extracted ecological source sites (2020) exhibited a pattern of “dense in the east and sparse in the west.” Compared with the results of the 2010 extraction, the source site area had shrunk by 0.87 % and number of sites had increased by 45.68 %. This implies that the shrinkage and fragmentation of source sites are still increasing, underscoring the urgent need for ecological corridors. (2) Compared to the pre-optimization period, the network node connectivity of the corridor network formed by the optimized 114 primary corridors and 131 secondary corridors has increased by 110 % and the network connectivity has increased by 112 %, which enhances the connectivity of the corridors. (3) From 2000 to 2020, the carbon stock in the Qilian Mountains decreased and then increased; with the construction of a corridor mountain range, the carbon stock can be further increased by 1.4748 million tons. Additionally, the Geographic Cellular Automata (GCA) model simulated that in 2030, the area of forest land in the Qilian Mountains, area of grassland, and carbon stock will increase by 71.75 km2, 313.87 km2, and 6.663 million tons, respectively. These results confirm that ecological protection and corridor construction promote ecological restoration and enhance carbon stocks in the Qilian Mountains, providing a scientific basis for future ecological projects and carbon stock analysis in the region.

Does self-containment of spatial scale and land use function contribute to mitigate urban heat island effects? Lessons from new towns in Shanghai

The concept of self-containment in new towns has been widely discussed from social and economic perspectives. However, localized interpretations within the context of China's development, particularly regarding climate adaptability and urban heat island (UHI) mitigation, are scarce. To fill this gap, our research analyzed self-containment from the perspectives of urban spatial scale and land use function. Focusing on Shanghai’s five new towns, we empirically demonstrated how self-containment influenced the UHI effects from 2005 to 2020, employing the Geodetector method. The findings reveal that during the daytime, the intensity of UHI in new towns decreased, serving as vital connectivity nodes of UHI within the region. Conversely, during the nighttime, both the intensity and area of UHI showed an increasing trend. The research confirmed that expanding the urban scale and functional diversity are effective strategies for mitigating the UHI. Based on these findings, we offer practical suggestions for the development of new towns: Increase population size while ensuring coordination with development scale; enhance mixed-use functions in large-scale development projects like university towns and industrial parks; and be vigilant of potential functional decline in central areas and increasing thermal impact due to new town development. Overall, this study enriches our understanding of self-containment in Chinese new towns and provides valuable insights for mitigating UHI in other similar contexts.

Between Fact and Fiction: Elizabeth II’s Funeral and Its Connection to The Crown on X (Twitter)

Television series enhance the social visibility of their content, as is the case with Queen Elizabeth II and The Crown. Netflix is the streaming television platform that has turned Peter Morgan’s successful series (2016) into a television icon where the monarch is the main protagonist, taking us on a biographical journey that mixes the historical and the political with fiction. The main character is made to seem more humane and is brought closer to the general public, all of which leads to a transmedia narrative. This research aims to analyze the content of the messages published on Twitter during the days surrounding the Queen’s State funeral in September 2022 and their connection with the series through the hashtag #TheCrown. The topics that have become trends worldwide are quantitatively analyzed, using different digital tools. The sample collected 1,489,279 tweets published during the days from the announcement of the death of Elizabeth II to the day of her funeral (from 8 to 19 September 2022). The results show nodes of connection between different players and linked communities to #TheCrown while offering the traffic generated by the hashtag with different nodes and edges.

Multi-order graph clustering with adaptive node-level weight learning

Current graph clustering methods emphasize individual node and edge connections, while ignoring higher-order organization at the level of motif. Recently, higher-order graph clustering approaches have been designed by motif-based hypergraphs. However, these approaches often suffer from hypergraph fragmentation issue seriously, which degrades the clustering performance greatly. Moreover, real-world graphs usually contain diverse motifs, with nodes participating in multiple motifs. A key challenge is how to achieve precise clustering results by integrating information from multiple motifs at the node level. In this paper, we propose a multi-order graph clustering model (MOGC) to integrate multiple higher-order structures and edge connections at node level. MOGC employs an adaptive weight learning mechanism to automatically adjust the contributions of different motifs for each node. This not only tackles hypergraph fragmentation issue but enhances clustering accuracy. MOGC is efficiently solved by an alternating minimization algorithm. Experiments on seven real-world datasets illustrate the effectiveness of MOGC.

Detection of minimal extended driver nodes in energetic costs reduction.

Structures of complex networks are fundamental to system dynamics, where node state and connectivity patterns determine the cost of a control system, a key aspect in unraveling complexity. However, minimizing the energy required to control a system with the fewest input nodes remains an open problem. This study investigates the relationship between the structure of closed-connected function modules and control energy. We discovered that small structural adjustments, such as adding a few extended driver nodes, can significantly reduce control energy. Thus, we propose MInimal extended driver nodes in Energetic costs Reduction (MIER). Next, we transform the detection of MIER into a multi-objective optimization problem and choose an NSGA-II algorithm to solve it. Compared with the baseline methods, NSGA-II can approximate the optimal solution to the greatest extent. Through experiments using synthetic and real data, we validate that MIER can exponentially decrease control energy. Furthermore, random perturbation tests confirm the stability of MIER. Subsequently, we applied MIER to three representative scenarios: regulation of differential expression genes affected by cancer mutations in the human protein-protein interaction network, trade relations among developed countries in the world trade network, and regulation of body-wall muscle cells by motor neurons in Caenorhabditis elegans nervous network. The results reveal that the involvement of MIER significantly reduces control energy required for these original modules from a topological perspective. Additionally, MIER nodes enhance functionality, supplement key nodes, and uncover potential mechanisms. Overall, our work provides practical computational tools for understanding and presenting control strategies in biological, social, and neural systems.

Tourist itineraries for Sustainable Mobility: an application in the Salento Area (Italy)

The purpose of this work is to define the design process, implemented through an analytical methodology, of two sustainable mobility tourist routes in a specific area of southern Italy (Salento); methodology applicable to other territorial realities. This design process was developed in the context of the 2014/2020 Interreg V-A Greece-Italy cross-border cooperation strategic project called "AI SMART_Adriatic Ionian Small Port Network". It takes into account the analysis of existing Plans and Programs at different levels (Sustainable Mobility Urban Plans; cycling routes; Territorial Plans, etc.), the analysis of statistical data in the area related to employment and tourism; the interlocution with key stakeholders, the analysis of architectural, landscape and food and wine surpluses. These routes have been defined for the interaction of the port of Otranto with the hinterland territories and with the opposite Ionian coast to promote the sustainable and inclusive transnational tourist fruition of the Apulian territory. The genesis of the two thematic itineraries stems from the need/willingness to valorise some areas of Salento through the connection of significant nodes in terms of landscape, architectural and historical value, which define two routes with Pulsano and Otranto as their vertices. The project brings together the potential of the Salento as an area with a strong tourist value, with the theme of sustainable mobility, suggesting the seasonality of travel of each, to meet the needs of deseasonalization of an area with a high summer and seaside tourism vocation.

The bay of La Isabela, Dominican Republic: The first enclave for the shelter, reception, construction and maintenance of ships in the New World, 1494–1498

La Isabela emerged as the first node or point of commercial connection between the ancient kingdoms of Spain and the Caribbean. This achievement was due to the meticulous spatial arrangement and planning of the settlement in the Isabeline cove, designed to facilitate interaction with both the surrounding environment and the maritime routes. Christopher Columbus achieved effective intercommunication between resource-rich areas strategically located for both maritime and river navigation. The subsidiary station of Marta, located in the Bajabonico Valley, played a crucial role as a complement to the residential centre of El Castillo, providing the basic inputs necessary for the functioning of the settlement and the shipbuilding industry. Additionally, the adjacent bay, with its arched configuration, served as a resting and recuperation place for the crew. At the opposite end, to the north-east, the shipyard was located in the La Playa area, equipped with personnel, resources and sufficient infrastructure for the construction, outfitting and repair of vessels.

Exploring Complicated Search Spaces With Interleaving-Free Sampling.

Conventional neural architecture search (NAS) algorithms typically work on search spaces with short-distance node connections. We argue that such designs, though safe and stable, are obstacles to exploring more effective network architectures. In this brief, we explore the search algorithm upon a complicated search space with long-distance connections and show that existing weight-sharing search algorithms fail due to the existence of interleaved connections (ICs). Based on the observation, we present a simple-yet-effective algorithm, termed interleaving-free neural architecture search (IF-NAS). We further design a periodic sampling strategy to construct subnetworks during the search procedure, avoiding the ICs to emerge in any of them. In the proposed search space, IF-NAS outperforms both random sampling and previous weight-sharing search algorithms by significant margins. It can also be well-generalized to the microcell-based spaces. This study emphasizes the importance of macrostructure and we look forward to further efforts in this direction. The code is available at github.com/sunsmarterjie/IFNAS.

Research on Mechanical Properties of New Aluminum Alloy Scaffold Nodes

This article explores the feasibility of using aluminum profiles to design a new type of foldable scaffolding system. To verify the ultimate bearing capacity and failure mode of structural node connectivity points, mechanical performance tests were conducted on the tensile strength of cast aluminum and angle iron nodes to clarify the ultimate bearing capacity and failure mode of the nodes, providing a reference for using aluminum profiles to make new types of scaffolding.

AOP-Anchored Transcriptome Analysis Catalogue Accelerates the Discovery of Environmental Toxicants in Zebrafish.

Current toxicity screening approaches to evaluate the vast number of environmental chemicals that require assessment are hampered due to their significant costs, time requirements, and reliance on live animal testing. The aim of the present study was to develop an adverse outcome pathway (AOP)-anchored transcriptome analysis (AATA) catalogue to expedite the discovery of environmental toxicants. 437 AOPs from the AOPwiki (https://aopwiki.org/) and 2280 transcriptomics data sets from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI ArrayExpress (AE) repositories were comprehensively reviewed and analyzed. By using the differentially expressed molecular key event (mKE) genes as connection nodes, we created a large-scale environmental substance─target gene (mKE)─predicted adverse outcomes (SGAs) network that included 78 substances, 1099 genes, and 354 adverse outcomes (AOs). To validate the reliability of the network, comprehensive literature verification was conducted. We demonstrated that 164 of the 354 AOs identified have been previously characterized in the literature. The results for 136 of these AOs were consistent with the predictions of the AATA catalogue, representing an accuracy rate of 82.9%. Besides, distinct patterns in molecular KEs and AOs among categories of substances, such as biocides and metals, were demonstrated. Some representative substances, including atrazine and copper, pose significant risks to fish at various levels of biological organization. Moreover, experimental verification of the AATA predictions was conducted, including exposures of zebrafish to perfluorooctanesulfonate, cresyl diphenyl phosphate, and lanthanum. Results demonstrated consistency with predictions of the AATA catalogue, with an accuracy rate of 92.3%. Collectively, the present findings support the AATA catalogue as an efficient and promising platform for identifying environmental toxicants to fish and thereby provide novel insights into the understanding of potential risks of environmental contaminants.

Graph isomorphism network with weighted multi‐aggregators for building shape classification

AbstractBuilding shape cognition is essential for tasks, such as map generalization, urban modeling, and building semantics and distribution pattern recognition. Traditional geometric and statistical methods rely on human‐defined shape indicators, and spectral‐based graph neural networks (GNNs) require Laplacian eigendecomposition, resulting in high algorithmic complexity. Therefore, we proposed a low‐complexity and simple‐to‐use spatial‐domain GNN for differentiating building shapes. To examine the influence of the building vertices on their shape, we treated each building as a graph and proposed a graph isomorphic network with weighted multi‐aggregators (GIN‐WMA) by analyzing the node connectivity of a building graph. The GIN‐WMA utilizes a novel aggregator that combines the sum and max aggregators, enhancing its recognition and differentiation capabilities. This approach can effectively differentiate nodes that have identical features after aggregation by the sum aggregator. We extracted features considering both local node and global shape features, drawing inspiration from Gestalt cognitive psychology and GNN's “node–graph” differentiation strategy. In addition, we compared the performance of GIN‐WMA with existing methods, studying the effect of various node features and their combinations on classification accuracy. The results demonstrated that GIN‐WMA outperforms other methods in discriminating building shapes, demonstrating superior capabilities in shape classification and enabling end‐to‐end extraction and classification of building shapes.

Multi-Disjoint Path opportunistic networks with Hidden Markov Chain modeling

Advancing the efficiency and reliability of wireless sensor networks is a paramount pursuit in modern networking research. In this context, we introduce a groundbreaking approach based on Hidden Markov Chain (HMC) modeling with opportunistic routing, harnessed by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism. Our innovative algorithm addresses key challenges in Wireless Body Area Networks, including delay, throughput, and bandwidth utilization. By strategically integrating the HMC model, which captures intricate state transitions and emission probabilities, our proposed method introduces a robust solution for optimizing node connections and routing decisions. This fusion of HMC and opportunistic routing capitalizes on the strengths of both paradigms, enhancing the network's ability to make intelligent decisions in dynamic scenarios. Through rigorous simulations, we showcase the algorithm's prowess in achieving efficient data transmission. The empirical evidence from these simulations underscores the algorithm's superiority when juxtaposed against state-of-the-art mechanisms such as Simple Multi-Packet Access (SMPA) and Priority-based Congestion-avoidance Routing Protocol (PCRP). Our algorithm not only outperforms existing solutions in terms of delay, throughput, and bandwidth utilization, but it also showcases its ability to adapt to varying network conditions, making it a versatile tool for diverse applications.

Root Exudates Promoted Microbial Diversity in the Sugar Beet Rhizosphere for Organic Nitrogen Mineralization

Rhizosphere environments play a vital role in the nutrient cycling of crops and soil organic nitrogen mineralization. Sugar beet is a highly nitrogen (N)-demanding crop, and it is necessary to explore the relationship between the sugar beet root exudates, the microbial community, and nitrogen utilization. In this study, a special separation method was employed to create rhizosphere (H3) and non-rhizosphere (H2 and H1) environments for sugar beet. After 50 d of cultivation in nearly inorganic-free soil, the microbial diversity and its correlation with root metabolites and N were examined. The results showed that in H3, the inorganic N content was over 23 times higher than in H1 and H2, with a 13.1% higher relative abundance of ammonia-oxidizing bacteria compared to H2 and a 32% higher abundance than H1. The relative abundance of nitrite-oxidizing bacteria was also 18.8% higher than in H1. Additionally, a significant positive correlation was observed between inorganic nitrogen content and serine (Ser) and isoleucine (Ile). The organic nitrogen content exhibited positive correlations with glycine (Gly), alanine (Ala), and tyrosine (Tyr) but displayed negative correlations with certain amino acids, organic acids, and glucose. The co-linearity network indicated that the microbial composition in H3 also exhibited higher node connectivity. It can be inferred that under the influence of sugar beet root exudates, the changes in the rhizosphere’s microbial diversity were more intricate, thereby benefiting soil nitrogen cycling and inorganic N accumulation. These findings provide profound insight into sugar beet soil organic nitrogen mineralization and contribute to the sustainable and environmentally friendly development of modern agriculture.

UPDATED MATHEMATICAL MODEL OF THE TRUNK PIPELINE SECTION WITH LOOPING IN CASE OF LEAKAGE

Pipeline transport is the main method of moving volumes of oil and oil products, as it has several advantages, for example, quality safety and high throughput. At the same time, it also has some disadvantages, which can include the difficulty of increasing throughput without high capital investments and possible leaks and unauthorized tie-ins into pipelines, which are sometimes difficult to identify. Looping is one of the main ways to increase the throughput of the existing pipeline system, when the need to increase the volume of oil and oil products supplies requires the construction of several parallel pipelines — looping. At the same time, the basic mathematical model for calculating the section of the trunk oil pipeline with looping does not fully cover all the physical processes associated with the distribution of flows in the parallel section, which arise due to turbulences in the connection nodes, as well as head loss along the length of the jumpers. Taking into account the resulting flow redistributions, it becomes difficult to detect a potential leak, since any of the detection methods can not solve the problem with high accuracy with determining the leak coordinate with an unknown distribution of fluid flow rates in a parallel section. As part of the study, an updated mathematical model of the oil export pipeline section with looping was developed, which took into account the presence of a potential leak in the parallel section. Mathematical modeling was carried out on the basis of the assumption of a known coordinate, the effect of leakage on the redistribution of flows in a parallel section was estimated. To assess the impact of the leak, various coordinates and volumetric flow rates were set, based on the calculation results, graphs of the flow rate in looping versus its length and internal diameter were plotted. The study is the initial phase of developing a hydraulic location leak detection system for the oil export pipeline section with looping. The need to refine the mathematical model for determining the flow rate in the parallel section is an urgent and necessary task, the solution of which will allow identifying potential leaks with reliable accuracy.