Connection Probability Research Articles

Topological and spectral properties of random digraphs.

We investigate some topological and spectral properties of Erdős-Rényi (ER) random digraphs of size n and connection probability p,D(n,p). In terms of topological properties, our primary focus lies in analyzing the number of nonisolated vertices V_{x}(D) as well as two vertex-degree-based topological indices: the Randić index R(D) and sum-connectivity index χ(D). First, by performing a scaling analysis, we show that the average degree 〈k〉 serves as a scaling parameter for the average values of V_{x}(D),R(D), and χ(D). Then, we also state expressions relating the number of arcs, largest eigenvalue, and closed walks of length 2 to (n,p), the parameters of ER random digraphs. Concerning spectral properties, we observe that the eigenvalue distribution converges to a circle of radius sqrt[np(1-p)]. Subsequently, we compute six different invariants related to the eigenvalues of D(n,p) and observe that these quantities also scale with sqrt[np(1-p)]. Additionally, we reformulate a set of bounds previously reported in the literature for these invariants as a function (n,p). Finally, we phenomenologically state relations between invariants that allow us to extend previously known bounds.

Conformal link prediction for false discovery rate control

AbstractMost link prediction methods return estimates of the connection probability of missing edges in a graph. Such output can be used to rank the missing edges from most to least likely to be a true edge, but does not directly provide a classification into true and nonexistent. In this work, we consider the problem of identifying a set of true edges with a control of the false discovery rate (FDR). We propose a novel method based on high-level ideas from the literature on conformal inference. The graph structure induces intricate dependence in the data, which we carefully take into account, as this makes the setup different from the usual setup in conformal inference, where data exchangeability is assumed. The FDR control is empirically demonstrated for both simulated and real data.

Security Performance Analysis of Full-Duplex UAV Assisted Relay System Based on SWIPT Technology

In this paper, a new methodology is developed for modeling and analyzing a full-duplex UAV-assisted relay system to facilitate solving the problems of UAV energy constraints and the vulnerability of UAVs to eavesdropping in the air. Combining simultaneous wireless information and power transfer (SWIPT) technology, we model the downlink UAV eavesdropping channel and propose a secure transmission protocol for a full-duplex UAV-assisted relay system. Using this transmission protocol, we analyze and derive the connectivity and security of the entire communication link, including connection probability and lower bounds on secrecy outage probability. A key intermediate step in our analysis is to derive the signal-to-digital noise ratio of the destination and eavesdropping nodes of the full-duplex UAV relay link. The analyses show that the power allocation factor λ is a trade-off between system connectivity and security, while greater eavesdropping interference needs to be sacrificed for an equal magnitude of security performance improvement under high security demand conditions.

Minor loss or huge threat? Assessment of potential infrastructure projects reveals drastic landscape degradation in the Atlantic forest

Projects demanding deforestation to expand urban infrastructure and road networks put pressure on the Atlantic Forest − a hyper-fragmented global conservation hotspot known for its high biodiversity and endemism. To support decision-making, we applied a scientific approach to analyse the immediate and long-term impacts of two major planned projects in the most threatened Atlantic Forest region – the Pernambuco Endemism Center, a crucial area for the survival of many endemic species. A Metropolitan Road Arch and a Training School for Career Sergeants are expected in the Environmental Protection Area Aldeia-Beberibe (31,634 ha) in the next few years. The area has 198 protected forest remnants, including one larger than 10,000 ha. Our analysis shows that the projects’ implementation may deforest 189 ha, increase fragment number by 3.5 % and decrease the average patch area by 4 %. The largest fragment will be divided into two. We may lose 335,307 trees and 44,655.59 Mg of carbon. Approximately 112.67 ha of watershed protection will be damaged. Thus, in the medium to long term, habitat quality will worsen, and the probability of connectivity will decrease, restricting forest-dependent species’ mobility considerably. To date, alternative areas have been proposed in meetings, working groups, and environmental advisory bodies for the analysed projects.We strongly oppose further deforestation and suggest that science-based approaches must be immediately considered for any project that requires forest suppression in highly fragmented landscapes. This is crucial to mitigate the alarming scenarios of extreme climate and biodiversity loss that we are currently witnessing in Brazil and worldwide.

Attracting Customers to District Heat Supply: The Case of Riga

District heating will be important in achieving future climate goals. Possibilities of using waste heat from different sources, e.g. subways, hospitals, shops, data centers, rivers are often discussed. Many district heating companies face the challenge of sufficient coverage of connected consumers in a city or region. To expand the operating area, companies should initially attract objects which are close to heat networks to lower the connection costs. The research question is how to attract existing buildings under construction to the district heating system. The present work uses system dynamics modeling for studying the possibilities of the Riga district heat supply company to increase consumer network. Modeling is based on historical data of residential buildings. The results show that old buildings choose to connect to the district heat supply when these are being renovated, or the individual heat supply equipment is out of order. The older the buildings, the more likely these will be connected to the district heating, however this decision may take at least 70 years. Renovation increases the probability of connection to the district heating, so the impact of subsidies for renovation is important. Regulation that requires connection to the district heating as a priority choice in case of renovation is also important.

Percolation of strongly correlated Gaussian fields, I: Decay of subcritical connection probabilities

Percolation of strongly correlated Gaussian fields, I: Decay of subcritical connection probabilities

Geometric random intersection graphs with general connection probabilities

Abstract Let $\mathcal{V}$ and $\mathcal{U}$ be the point sets of two independent homogeneous Poisson processes on $\mathbb{R}^d$ . A graph $\mathcal{G}_\mathcal{V}$ with vertex set $\mathcal{V}$ is constructed by first connecting pairs of points (v, u) with $v\in\mathcal{V}$ and $u\in\mathcal{U}$ independently with probability $g(v-u)$ , where g is a non-increasing radial function, and then connecting two points $v_1,v_2\in\mathcal{V}$ if and only if they have a joint neighbor $u\in\mathcal{U}$ . This gives rise to a random intersection graph on $\mathbb{R}^d$ . Local properties of the graph, including the degree distribution, are investigated and quantified in terms of the intensities of the underlying Poisson processes and the function g. Furthermore, the percolation properties of the graph are characterized and shown to differ depending on whether g has bounded or unbounded support.

Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach

AbstractThe efficient development of Mobile Wireless Sensor Networks (MWSNs) relies heavily on optimizing two key parameters: Throughput and Energy Consumption. The proposed work investigates network connectivity issues with MWSN and proposes two routing algorithms, namely Self‐Organising Maps based‐Optimised Link State Routing (SOM‐OLSR) and Deep Reinforcement Learning based‐Optimised Link State Routing (DRL‐OLSR) for MWSNs. The primary objective of the proposed algorithms is to achieve energy‐efficient routing while maximizing throughput. The proposed algorithms are evaluated through simulations by considering various performance metrics, including connection probability (CP), end‐to‐end delay, overhead, network throughput, and energy consumption. The simulation analysis is discussed under three scenarios. The first scenario undertakes ‘no optimisation’, the second considers SOM‐OLSR, and the third undertakes DRL‐OLSR. A comparison between DRL‐OLSR and SOM‐OLSR reveals that the former surpasses the latter in terms of low latency and prolonged network lifetime. Specifically, DRL‐OLSR demonstrates a 47% increase in throughput, a 67% reduction in energy consumption, and a CP three times higher than SOM‐OLSR. Furthermore, when contrasted with the ‘no optimisation’ scenario, DRL‐OLSR achieves a remarkable 69.7% higher throughput and nearly 89% lower energy consumption. These findings highlight the effectiveness of the DRL‐OLSR approach in wireless sensor networks.

Sparse subspace clustering in diverse multiplex network model

The paper considers the DIverse MultiPLEx (DIMPLE) network model, where all layers of the network have the same collection of nodes and are equipped with the Stochastic Block Models. In addition, all layers can be partitioned into groups with the same community structures, although the layers in the same group may have different matrices of block connection probabilities. To the best of our knowledge, the DIMPLE model, introduced in Pensky and Wang (2021), presents the most broad SBM-equipped binary multilayer network model on the same set of nodes and, thus, generalizes a multitude of papers that study more restrictive settings. Under the DIMPLE model, the main task is to identify the groups of layers with the same community structures since the matrices of block connection probabilities act as nuisance parameters under the DIMPLE paradigm. The main contribution of the paper is achieving the strongly consistent between-layer clustering by using Sparse Subspace Clustering (SSC), the well-developed technique in computer vision. In addition, SSC allows to handle much larger networks than spectral clustering, and is perfectly suitable for application of parallel computing. Moreover, our paper is the first one to obtain precision guarantees for SSC when it is applied to binary data.

Inhomogeneous long-range percolation in the weak decay regime

We study a general class of percolation models in Euclidean space including long-range percolation, scale-free percolation, the weight-dependent random connection model and several other previously investigated models. Our focus is on the weak decay regime, in which inter-cluster long-range connection probabilities fall off polynomially with small exponent, and for which we establish several structural properties. Chief among them are the continuity of the bond percolation function and the transience of infinite clusters.

ОЦІНЮВАННЯ ВПЛИВУ ЗАСОБІВ ПІДКЛЮЧЕННЯ РЕЗЕРВУ НА ПОКАЗНИКИ НАДІЙНОСТІ СИСТЕМИ ПЕРЕДАВАННЯ ДАНИХ

Structural redundancy is included in modern data transmission systems to ensure the specified reliability indicators. Structural redundancy gives the system fault tolerance and significantly increases its reliability. To manage the redundant resource, it is necessary to have additional tools that will connect the redundant equipment to the main configuration of the data transmission system. The redundant equipment is a separate technical means and, from the point of view of reliability, it is connected in series with other modules of the data transmission system. Thus, the inclusion of redundant connection facilities in the data transmission system reduces its reliability. In addition, even with perfect reliability, redundant connections may fail or malfunction and, under certain conditions, cause the transmission system to fail. To determine the impact of reserve connection facilities on the reliability of the transmission system, the article applies an original technology for building models of discrete-continuous stochastic systems. ASNA software was used as a software tool for modeling the reliable behavior of the data transmission system. This software provides automated construction of the model in the form of a graph of states and transitions based on a structural automaton model. Based on the graph of states and transitions, an analytical model is automatically generated in the form of a system of linear differential Kolmogorov-Chapman equations. Using the developed model, the article quantitatively analyzes the impact of redundant connection means (switches) on the reliability indicators of the data transmission system. In the study, the proposed models, unlike the existing ones, take into account both reliability indicators (probability of failure) and functionality indicators (probability of switching, speed) of the reserve connection means. On the basis of the developed model, the limit values of reliability and functionality of the redundant connection means (switches) were determined, at which they will minimize the reliability of the fault-tolerant data transmission system. Based on the analysis of the obtained results, practical recommendations for the selection of reliability indicators of the backup connection means, their speed and probability of switching are formed.

A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding

The rapid development of high-throughput technology has generated a large amount of protein–protein interaction (PPI) data, which provide a large amount of data support for constructing dynamic protein–protein interaction networks (PPINs). Constructing dynamic PPINs and applying them to recognize protein complexes has become a hot research topic. Most existing methods for complex recognition cannot fully mine the information of PPINs. To address this problem, we propose a construction method of dynamic weighted protein network by multi-level embedding (DWPNMLE). It can reflect the protein network’s dynamics and the protein network’s higher-order proximity. Firstly, the protein active period is calculated to divide the protein subnetworks at different time points. Then, the connection probability is used for the proteins possessing the same time points to judge whether there is an interaction relationship between them. Then, the corresponding protein subnetworks (multiple adjacency matrices) are constructed. Secondly, the multiple feature matrices are constructed using one-hot coding with the gene ontology (GO) information. Next, the first embedding is performed using variational graph auto-encoders (VGAEs) to aggregate features efficiently, followed by the second embedding using deep attributed network embedding (DANE) to strengthen the node representations learned in the first embedding and to maintain the first-order and higher-order proximity of the original network; finally, we compute the cosine similarity to obtain the final dynamic weighted PPIN. To evaluate the effectiveness of DWPNMLE, we apply four classical protein-complex-recognition algorithms on the DWPNMLE and compare them with two other dynamic protein network construction methods. The experimental results demonstrate that DWPNMLE significantly enhances the accuracy of complex recognition with high robustness, and the algorithms’ efficiency is also within a reasonable range.

An Efficient Fault-Tolerant Vehicular Data Forwarding Scheme for Timely and Reliable Data Transmission

With the increasing demands of various applications on vehicles, such as road condition sensing, traffic management, location-based services, and others both academic researchers and automotive industries pay much attention to Vehicular Networks. Due to the rapid change of network topology and intermittent connection, it is a big challenge to satisfy the service requests from a large number of vehicular users. Further, network faults disrupt communication among vehicles, which affects the reliability and timely data delivery in vehicular networks. In this paper, for timely and reliable data transmission, an efficient data forwarding scheme for vehicular networks is proposed. Also, the paper addresses three types of network faults along with the data forwarding issue by selecting an optimal route with a higher probability of connectivity and lower experienced delay. The proposed scheme detects the faults dynamically and takes necessary actions to recover from the fault. Extensive simulation has been performed to evaluate the proposed scheme. Compared to the existing AODV- D approach and SRCP scheme, the proposed scheme attains better performance in terms of packet delivery ratio, end-to-end delay, and control overhead.

Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit.

The computational capabilities of neuronal networks are fundamentally constrained by their specific connectivity. Previous studies of cortical connectivity have mostly been carried out in rodents; whether the principles established therein also apply to the evolutionarily expanded human cortex is unclear. We studied network properties within the human temporal cortex using samples obtained from brain surgery. We analyzed multineuron patch-clamp recordings in layer 2-3 pyramidal neurons and identified substantial differences compared with rodents. Reciprocity showed random distribution, synaptic strength was independent from connection probability, and connectivity of the supragranular temporal cortex followed a directed and mostly acyclic graph topology. Application of these principles in neuronal models increased dimensionality of network dynamics, suggesting a critical role for cortical computation.

Deteriorating habitat suitability and connectivity of waterbirds in the Bohai Sea Rim: Consequences of land use transformation

Wetlands in the Bohai Sea Rim play an indispensable role in maintaining the stability of waterbird populations on the East Asian-Australian flyway. However, motivated by economic development and urban expansion, coastal wetlands in this region have been extensively reclaimed, leading to a decline in the area and degradation of natural wetlands over the past two decades. We analyzed the temporal and spatial changes of suitable habitats of different groups of waterbirds and quantified habitat connectivity changes and the importance of different patches by using the Probability of Connectivity. The results show that the suitable habitat area of all the different groups of waterbirds decreased sharply from 2000 to 2020. In addition, the utilization ratio by waterbirds of artificial habitats increased. Land use is the primary environmental variable determining the distribution of suitable habitats. Meanwhile, the connectivity of habitats of different groups of waterbirds has also been declining. However, the patches of Tianjin and Hebei in Bohai Bay still have high importance for waterbirds, particularly shorebirds. Expanding existing protection areas to cover the above-mentioned patches is recommended. We also advocate stricter protection measures and necessary ecological restoration activities on both the natural and artificial wetlands.

Can large-scale tree planting in China compensate for the loss of climate connectivity due to deforestation?

Extensive deforestation has been a major reason for the loss of forest connectivity, impeding species range shifts under current climate change. Over the past decades, the Chinese government launched a series of afforestation and reforestation projects to increase forest cover, yet whether the new forests can compensate for the loss of connectivity due to deforestation—and where future tree planting would be most effective—remains largely unknown. Here, we evaluate changes in climate connectivity across China's forests between 2015 and 2019. We find that China's large-scale tree planting alleviated the negative impacts of forest loss on climate connectivity, improving the extent and probability of climate connectivity by 0–0.2 °C and 0–0.03, respectively. The improvements were particularly obvious for species with short dispersal distances (i.e., 3 km and 10 km). Nevertheless, only ~55 % of the trees planted in this period could serve as stepping stones for species movement. This indicates that focusing solely on the quantitative target of forest coverage without considering the connectivity of forests may miss opportunities in tree planting to facilitate climate-induced range shifts. More attention should be paid to the spatial arrangement of tree plantations and their potential as stepping stones. We then identify priority areas for future tree planting to create effective stepping stones. Our study highlights the potential of large-scale tree planting to facilitate range shifts. Future tree-planting efforts should incorporate the need for species range shifts to achieve more biodiversity conservation benefits under climate change.

Fragmentation and Connectivity in dehesa Ecosystems Associated with Cerambyx spp. Dispersion and Control: A Graph-Theory Approach

Xylophagous insects play a crucial role in forest ecosystems, contributing to population dynamics. The “Cerambyx complex” (CC) constitutes an emerging pest in Mediterranean oak woodlands. We studied the fragmentation and connectivity of holm and cork oak stands in Andalusia (Spain), and the relationships with the current dispersion of CC, as well as the effect on the connectivity and dispersion patterns with the implementation of nests of a predator bird (Garrulus glandarius) to reduce insect populations in highly connected areas. The Kernel Density Estimation (KDE) was used to assess the spatial distribution of CC. Connectivity was assessed using graphs theory (Graphab 2.6) to characterize the importance of patches and linkages for contributing to dispersal. We selected the Eurasian jay (G. glandarius) as a reference bird species to generate “barriers” to the dispersion of the CC. We used the probability of connectivity (PC) and the flux (F) to compare the effect of the introduction of Eurasian jay nets. Results showed an increasing trend in the distribution and incidence of CC during the period 2001–2016, resulting in 7.3% and 13.1% mortality rates for Q. ilex and Q. suber, respectively. The connectivity model using only Q. ilex and Q. suber forests as reference habitats was not enough to explain the distribution of CC. The value of PC and F metrics decreased by 38.09% and 20.59% by introducing 300 nests of Eurasian jay. Our methodology provides a pest management tool using connectivity metrics, which can be integrated with other variables to control pest outbreaks and pest dispersion.

Ultra-Short-Term Power Prediction of Large Offshore Wind Farms Based on Spatiotemporal Adaptation of Wind Turbines

Accurately predicting the active power output of offshore wind power is of great significance for reducing the uncertainty in new power systems. By utilizing the spatiotemporal correlation characteristics among wind turbine unit outputs, this paper embeds the Diffusion Convolutional Neural Network (DCNN) into the Gated Recurrent Unit (GRU) for the feature extraction of spatiotemporal correlations in wind turbine unit outputs. It also combines graph structure learning to propose a sequence-to-sequence model for ultra-short-term power prediction in large offshore wind farms. Firstly, the electrical connection graph within the wind farm is used to preliminarily determine the reference adjacency matrix for the wind turbine units within the farm, injecting prior knowledge of the adjacency matrix into the model. Secondly, a convolutional neural network is utilized to convolve the historical curves of units within the farm along the time dimension, outputting a unit connection probability vector. The Gumbel–softmax reparameterization method is then used to make the probability vector differentiable, thereby generating an optimal adjacency matrix for the prediction task based on the probability vector. At the same time, the difference between the two adjacency matrices is added as a regularization term to the loss function to reduce model overfitting. The simulation of actual cases shows that the proposed model has good predictive performance in ultra-short-term power prediction for large offshore wind farms.

Connection probabilities of multiple FK-Ising interfaces

We find the scaling limits of a general class of boundary-to-boundary connection probabilities and multiple interfaces in the critical planar FK-Ising model, thus verifying predictions from the physics literature. We also discuss conjectural formulas using Coulomb gas integrals for the corresponding quantities in general critical planar random-cluster models with cluster-weight q∈[1,4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${q \\in [1,4)}$$\\end{document}. Thus far, proofs for convergence, including ours, rely on discrete complex analysis techniques and are beyond reach for other values of q than the FK-Ising model (q=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q=2$$\\end{document}). Given the convergence of interfaces, the conjectural formulas for other values of q could be verified similarly with relatively minor technical work. The limit interfaces are variants of SLEκ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SLE}_\\kappa $$\\end{document} curves (with κ=16/3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa = 16/3$$\\end{document} for q=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q=2$$\\end{document}). Their partition functions, that give the connection probabilities, also satisfy properties predicted for correlation functions in conformal field theory (CFT), expected to describe scaling limits of critical random-cluster models. We verify these properties for all q∈[1,4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q \\in [1,4)$$\\end{document}, thus providing further evidence of the expected CFT description of these models.

Spatial and Temporal Dynamics of the Vegetation Cover from the Bijagual Massif, Boyacá, Colombia, during the 1986-2021 Period.

Landscape changes based on spectral responses allow showing plant cover changes through diversity, composition, and ecological connectivity. The spatial and temporal vegetation dynamics of the Bijagual Massif from 1986 to 2021 were analyzed as a measure of ecological integrity, conservation, and territory. The covers identified were high open forest (Hof), dense grassland of non-wooded mainland (Dgnm), a mosaic of pastures and crops (Mpc), lagoons (Lag), and bare and degraded lands (Bdl). The Bijagual Massif has 8574.1 ha. In 1986, Dgnm occupied 42.6% of the total area, followed by Mpc (32.8%) and Hof (24.5%); by 2000, Mpc and Hof increased (43.7 and 28.1%, respectively), while Dgnm decreased (28%); by 2021, Dgnm was restricted to the northeastern zone and continued to decrease (25.2%), Mpc occupied 52.9%, Hof 21.7% and Bdl 0.1%. Of the three fractions of the connectivity probability index, only dPCintra and dPCflux contribute to ecological connectivity. Hof and Dgnm show patches with biota habitat quality and availability. Between 1986 and 2021, Dgnm lost 1489 ha (41%) and Hof 239.5 ha (11%). Mpc replaced various covers (1722.2 ha; 38%) in 2021. Bijagual has a valuable biodiversity potential limited by Mpc. Territorial planning and sustainable agroecological and ecotourism proposals are required due to the context of the ecosystems.