Connection Probability Research Articles

User Identification Across Multiple Social Networks Based on Naive Bayes Model.

Recently, the problem of user identification across multiple social networks (UIAMSNs) has attracted considerable attention since it is a prerequisite for many downstream tasks and applications. Although substantial network feature-based approaches have been proposed to solve the UIAMSNs' problem, the matching degree in most of the current works is given by experience, which lacks a solid theoretical basis. To alleviate the above predicament, we propose a user identification algorithm based on naive Bayes model (UI-NBM) within the network feature-based framework. First, a matching degree index is designed based on the naive Bayes model, which can accurately measure the contributions of different common matched node pairs (MNPs) to the connection probability of unmatched node pairs (UMNPs). Second, the matching degrees of all UMNPs are formulated as the product of matrices, giving rise to the great reduction of the time complexity and the compact expression; Finally, with the idea of recursion process, more UMNPs can be iteratively predicted even when only a small amount of prior information (i.e., a few number of MNPs) is known. The experimental results on the synthetic and real cross platforms demonstrate that the method outperforms the baseline methods within the feature-based framework.

White matter tract alterations in schizophrenia identified by DTI-based probabilistic tractography: a multisite harmonisation study.

It has been suggested that schizophrenia involves dysconnectivity between functional brain regions and also the white matter structural disorganisation. Thus, diffusion tensor imaging (DTI) has widely been used for studying schizophrenia. However, most previous studies have used the region of interest (ROI) based approach. We, therefore, performed the probabilistic tractography method in this study to reveal the alterations of white matter tracts in the schizophrenia brain. A total of four different datasets consisted of 189 patients with schizophrenia and 213 healthy controls were investigated. We performed retrospective harmonisation of raw diffusion MRI data by dMRIharmonisation and used the FMRIB Software Library (FSL) for probabilistic tractography. The connectivities between different ROIs were then compared between patients and controls. Furthermore, we evaluated the relationship between the connection probabilities and the symptoms and cognitive measures in patients with schizophrenia. After applying Bonferroni correction for multiple comparisons, 11 different tracts showed significant differences between patients with schizophrenia and healthy controls. Many of these tracts were associated with the basal ganglia or cortico-striatal structures, which aligns with the current literature highlighting striatal dysfunction. Moreover, we found that these tracts demonstrated statistically significant relationships with few cognitive measures related to language, executive function, or processing speed. We performed probabilistic tractography using a large, harmonised dataset of diffusion MRI data, which enhanced the statistical power of our study. It is important to note that most of the tracts identified in this study, particularly callosal and cortico-striatal streamlines, have been previously implicated in schizophrenia within the current literature. Further research with harmonised data focusing specifically on these brain regions could be recommended.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits.

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

A method to predict connectivity for nomadic waterbird species from tracking data

ContextNomadic waterbird species move erratically, which makes it difficult to predict site use and connectivity over time. This is particularly pertinent for long-distance movements, during which birds may move between sites hundreds to thousands of kilometres apart.ObjectivesThis study aimed to understand how landscape and weather influence long-distance waterbird movements, to predict the probability of connectivity between locations and forecast short-term movements for a nomadic species, the straw-necked ibis (Threskiornis spinicollis) in Australia’s Murray–Darling basin.MethodsWe used 3.5 years of satellite tracking data together with high-resolution landscape and weather variables to model the expected distance travelled under environmental scenarios for long-distance movements. We generated least-cost paths between locations of interest and simulated the probability that birds could exceed the least cost-distance as a measure of connectivity. We also generated short-term forecasts (1–3 days; conditional on departure) of the probability of bird occurrence at a location given the expected environmental conditions.ResultsOur results suggested that wind is the dominant predictor of distance travelled during long-distance movements, with significant but smaller effects from month. Birds travelled further when wind benefit was higher and during summer. Further work is required to validate our forecasts of bird positions over short time periods.ConclusionsOur method infers the predictors of poorly understood movements of nomadic birds during flight. Understanding how partial migrants use landscapes at large scales will help to protect birds and the landscapes where they live.

The connectivity threshold of the wetlands based on water environment response, and a case study of China’s Baiyangdian wetland

The quality of the water environment of wetlands is closely related to the changes in hydrologic connectivity, so wetland responses to changing connectivity exhibit a range of optimal connectivity values. Identifying these ranges of values is of great significance for wetland ecological restoration. Current studies mostly determine the hydrologic connectivity ranges based on a single target, even though connectivity simultaneously affects multiple water environment targets. Hence, the optimal hydrologic connectivity should be determined by accounting for multiple targets. With the Baiyangdian Wetland as the study area, we developed a method to model how the water environment responded to changes in hydrologic connectivity from three perspectives: hydrodynamics, water quality, and aquatic habitat. To do so, we used the BP neural network model and a non-dominated sorting genetic algorithm to calculate the hydrologic connectivity values in a context with multiple targets. We found that under the optimal hydrodynamic target, the integral index of connectivity (IIC) threshold was [0.23, 0.61] and the probability connectivity (PC) threshold was [0.32, 0.68]; under the optimal water quality target, the IIC threshold was [0.42, 0.70] and the PC threshold was [0.48, 0.82]; under the optimal aquatic habitat target, the IIC threshold was [0.38, 0.69] and the PC threshold was [0.35, 0.71]; and under the comprehensive optimization target, the IIC threshold was [0.48, 0.64] and the PC threshold was [0.51, 0.73]. These ranges of values will help wetland managers determine the optimal ecological water releases to meet the ecosystem quality targets.

GNN-IR: Examining graph neural networks for influencer recommendations in social media marketing

With the notable growth of the Internet, a number of platforms have emerged and attracted an enormous number of users. Based on the impact of these platforms, some ‘influencers’ are highlighted. These influencers wield significant power, shaping consumer behavior. This influence spawned the concept of influencer marketing, where companies leverage these personalities to advertise their products. YouTube stands out as a prominent platform in this trend. However, considering the limited number of influencers and their concepts, the majority of companies, which hope to conduct their marketing campaigns with influencers face challenges in identifying suitable influencers for their campaigns. With this trend, we introduce GNN-IR, a graph neural network for influencer recommendation, based on the connections between companies and influencers of YouTube, one of the largest content platforms. In developing GNN-IR, we adopted a data-driven methodology utilizing a meticulously curated dataset collected in-house. Our dataset comprises a total of 25,174 relationship entries between advertisers and influencers, involving 1,886 distinct advertisers and 3,812 unique YouTube influencers. It encompasses diverse data modalities, including images, text, and assorted metadata. The data was sourced from two primary platforms: YouTube and ugwanggi. Ugwanggi provided valuable insights into the relationships between advertisers and influencers via their information. Meanwhile, YouTube offered more comprehensive and detailed influencer-centric information. We employed PyTorch Geometric to construct a bipartite graph representing interconnected data. Our recommendation system operates via link prediction, suggesting the Top-k influencers to advertisers based on the calculated connection probability between nodes. To assess GNN-IR's performance, we employed a range of evaluation metrics. For link prediction, we measured Accuracy, Precision, Recall, and F1-score. Additionally, in the recommendation phase, we evaluated Precision@k, Recall@k, and F1-score@k. Using GNN-IR and incorporating profile images from YouTube, keyword features, metadata, and sentiment gleaned from YouTube comments, we achieved Precision levels of 96.51% at k=1 and 93.68% at k=10. Based on the experimental results, several implications and limitations are presented. The collected dataset is publicly available at https://github.com/dxlabskku/GNN-IR.git.

Generalized latent space model for one-mode networks with awareness of two-mode networks

Latent space models have been widely studied for one-mode networks, in which the same type of nodes connect with each other. In many applications, one-mode networks are often observed along with two-mode networks, which reflect connections between different types of nodes and provide important information for understanding the one-mode network structure. However, the classical one-mode latent space models have several limitations in incorporating two-mode networks. To address this gap, a generalized latent space model is proposed to capture common structures and heterogeneous connecting patterns across one-mode and two-mode networks. Specifically, each node is embedded with a latent vector and network-specific degree parameters that determine the connection probabilities between nodes. A projected gradient descent algorithm is developed to estimate the latent vectors and degree parameters. Moreover, the theoretical properties of the estimators are established and it has been proven that the estimation accuracy of the shared latent vectors can be improved through incorporating two-mode networks. Finally, simulation studies and applications on two real-world datasets demonstrate the usefulness of the proposed model.

Gaussian mixture model based reconstruction of undirected networks

The reconstruction of network structure from data represents a significant scientific challenge in the field of complex networks, which has attracted considerable attention from the research community. The most of existing network reconstruction methods transform the problem into a series of linear equation systems, to solve the equations. Subsequently, truncation methods are used to determine the local structure of each node by truncating the solution of each equation system. However, truncation methods frequently exhibit inadequate accuracy, and lack methods of evaluating the truncatability of solutions to each system of equations, that is to say, the reconstructability of nodes. In order to address these issues, in this work an undirected network reconstruction method is proposed based on a Gaussian mixture model. In this method, a Gaussian mixture model is first used to cluster the solution results obtainedby solving a series of linear equations, and then the probabilities of the clustering results are utilized to depict the likelihood of connections between nodes. Subsequently, an index of reconstructibility is defined based on information entropy, thus the probability of connections between each node and other nodes can be used to measure the reconstructibility of each node. The proposed method is ultimately applied to undirected networks. Nodes identified with high reconstructibility are used as a training set to guide the structural inference of nodes with lower reconstrucibility, thus enhancing the reconstruction of the undirected network. The symmetrical properties of the undirected network are then employed to infer the connection probabilities of the remaining nodes with other nodes. The experiments on both synthetic and real data are conducted and a variety of methods are used for constructing linear equations and diverse dynamical models. Compared with the results from a previous truncated reconstruction method, the reconstruction outcomes are evaluated. The experimental results show that the method proposed in this work outperforms existing truncation reconstruction methods in terms of reconstruction performance, thus confirming the universality and effectiveness of the proposed method.

Контекстный анализ связности двухполюсных структур

The paper discusses an original approach to the analysis of network structures of various natures, which are represented in the form of graphs. It is assumed that the probability of connectivity of individual edges functionally depends on their physical characteristics (edge length) and tends to zero. The issue of increasing the connectivity probability of the entire graph is being addressed, which is defined as the probability of the existence of a sequential set of edges connecting the selected start and end vertices. A distinctive feature of the proposed method is the move away from the traditional reservation of obviously weak connection points towards a functional impact on individual connections of the structure, changing their physical characteristics. Based on the theory of systems functioning and the theory of dominants, contextual approach is being developed aimed at identifying the dominant connections of the graph, reflecting the functional and meaningful meaning of the connections between the vertices of the original modeled object. As a result, a set of dominants S is identified, consisting of edges whose parameters meet the given criteria. The selection criterion is the length of the edge included in the asymptotic relation, which characterizes the connectivity probability of the entire graph. It has been proven that changing the length of edges from a given set by ε > 0 increases the probability of connectedness of the original graph in the maximum case in h-ε or h-εβ times, where h → 0 and β is a parameter depending on the number of graph paths passing through this edge. Various methods have been proposed to increase the connectivity probability of the graph under consideration: from the point of view of the point effect, by reducing the length of a single edge from the set S; from the position of influencing the maximum set of edges from S, allowing to obtain the overall maximum effect. A comparative analysis of the proposed ways to increase the probability of graph connectivity has been carried out, and the appropriate conditions have been identified to achieve the maximum effect from the selected methods of influence.

Diversity-induced trivialization and resilience of neural dynamics.

Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.

Directed network-based connectivity probability evaluation for urban bridges

Directed network-based connectivity probability evaluation for urban bridges

Quantification of bedrock structural controls of longitudinal sediment connectivity using the probability of connectivity and sediment continuity model

The structural controls of sediment connectivity are poorly understood in low-gradient watersheds and stream networks. We developed a field- and modeling-based framework to understand the timescales and thresholds associated with the structural controls of sediment connectivity and continuity in the Inner Bluegrass region of Kentucky, USA. Field reconnaissance was carried out to assess stream morphology and sediment connectivity, which was leveraged to derive instream sediment connectivity indices for 181 pool-riffle sequences in the low-gradient bedrock stream network. Our model framework coupled a probability-based connectivity formulation with the sediment continuity formula to assess sediment transport and connectivity in the stream network. Results indicated that the bedrock system reflects an end-member in which lithology controls longitudinal stream morphology. Specifically, we found that regional morphology controls pool-riffle length scales via bedrock outcrops, which therein controls the timescales of sediment connectivity over multiple hydrologic regimes. The pool to riffle length ratio was found to be 4.6, an order of magnitude greater than the ratio for alluvial systems. The new formulation of the sediment continuity formula coupled with the probability of connectivity framework adequately captured both short-term and long-term continuous dynamics of sediment transport, as indicated by evaluation metrics with measured sediment flux and field reconnaissance. Based on the analysis of 362 riffles and pools, we found that sediment in pools upstream of bedrock outcrop barriers are disconnected 93 % of the time whereas riffles are connected and allow full continuity 99 % of the time. Longitudinal sediment connectivity within the stream network was found to be controlled by both functional connectivity and structural connectivity, which contrasts recent findings describing the controls of sediment connectivity over lateral pathways on hillslopes.

Greedy routing optimisation in hyperbolic networks

Finding the optimal embedding of networks into low-dimensional hyperbolic spaces is a challenge that received considerable interest in recent years, with several different approaches proposed in the literature. In general, these methods take advantage of the exponentially growing volume of the hyperbolic space as a function of the radius from the origin, allowing a (roughly) uniform spatial distribution of the nodes even for scale-free small-world networks, where the connection probability between pairs decays with hyperbolic distance. One of the motivations behind hyperbolic embedding is that optimal placement of the nodes in a hyperbolic space is widely thought to enable efficient navigation on top of the network. According to that, one of the measures that can be used to quantify the quality of different embeddings is given by the fraction of successful greedy paths following a simple navigation protocol based on the hyperbolic coordinates. In the present work, we develop an optimisation scheme for this score in the native disk representation of the hyperbolic space. This optimisation algorithm can be either used as an embedding method alone, or it can be applied to improve this score for embeddings obtained from other methods. According to our tests on synthetic and real networks, the proposed optimisation can considerably enhance the success rate of greedy paths in several cases, improving the given embedding from the point of view of navigability.

Physical Layer Security in IRS-Assisted Cache-Enabled Satellite Communication Networks

This paper presents a comprehensive analysis of the physical layer security performance of a cache-enabled satellite communication network that incorporates intelligent reflecting surfaces (IRS) in the presence of a passive eavesdropper. In the proposed system, content caches are deployed at both the ground station and the satellite, which can improve system performance by reducing latency and transmission overhead. Moreover, the use of IRS provides an additional layer of security by enabling the manipulation of the reflected signals to impede eavesdropping. Practical channel models are used to derive connection probability and secrecy probability for both the ground station-IRS-user and the satellite-IRS-user links. The obtained results are then used to evaluate the system’s secure transmission probability, which is maximized subject to the caching probabilities and transmission rate constraints. The paper presents numerical results to demonstrate the accuracy of the analysis and the effectiveness of deploying IRS and caching to support secure content delivery. The findings provide valuable insights into the potential benefits of utilizing IRS and caching technologies in satellite communication networks for improved physical layer security.

Evolution of neuron firing and connectivity in neuronal plasticity with application to Parkinson’s disease

We unify different modeling structures for neuron state evolution showing how they can be derived from an invariance requirement imposed to a dissipation inequality under diffeomorphism-based changes of observer in the physical space. This is a completely non-standard way of using the second law of thermodynamics, although in isothermal setting, a law otherwise commonly used for determining constitutive restrictions and admissibility conditions as those pertaining to shock waves. In this setting, we also consider a time-varying neuronal connectivity and derive the consistent structure of its evolution equation from the same invariance principle. In the case of Parkinson, the connectivity depends also on the distribution of calcium channels that bring dopamine excess. Under special conditions, we show how the connectivity probability distribution changes in time and is influenced by the one of calcium channels. We account for memory effects in the cortical matter, namely dependence on firing and connectivity histories.

Forest landscape connectivity to prioritize afforestation in urban ecosystems: Seoul as a case study

Forest landscape connectivity is a key indicator that reflects the quality and function of the ecological environment. In urban ecosystems, analysis frameworks are needed to prioritize and optimize afforestation projects that maintain and increase forest landscape connectivity to help mitigate the increasing fragmentation of urban habitats, coordinate the relationship between urban development and urban ecosystem services, and guide decision making processes. The objective of this study is to identify forest patches that are most worth conserving and small patches that make the most significant contribution to landscape connectivity after their transformation into forest patches in a high-density city. We conducted a case study in Seoul, South Korea, where we initially quantified the landscape connectivity changes from 2001 to 2018 using the probability of connectivity (PC) and integral index of connectivity indices. Subsequently, we calculated the importance of each forest patch for four different years and ranked the potential contribution of each patch based on data from 2018 using the delta values for PC index (dPC). Our results showed that forest landscape connectivity in Seoul increased significantly from 2001 to 2018. In all four years, the ten forest patches with the highest dPC values remained nearly unchanged and were large in size; however, the individual area of each patch was not strictly related to its importance for connectivity. Based on these findings, we further examined the potential effect that the afforestation of habitat patches smaller than 20 ha would have on the overall forest landscape connectivity in Seoul. We also identified the 50 most important patches for afforestation using six land use and land cover types to provide a guide for effective afforestation efforts in Seoul. We expect that this study could enhance the effectiveness of habitat connectivity intervention while providing a realistic strategy for ecological afforestation planning in urban ecosystems.

Transmission reliability evaluation of wireless sensor networks considering channel capacity randomness and energy consumption failure

The reliable transmission of data packets is the basis of ensuring the Internet of Things applications. Therefore, how to ensure the transmission reliability of wireless sensor networks has already become a hotspot. However, the existing researches ignore the effects of multiple fault modes and random factors on transmission reliability. Firstly, in the node reliability modeling, few studies have considered the effects of random failure and energy consumption failure comprehensively. Secondly, in the link reliability modeling, existing studies ignore the effect of the channel capacity randomness, which can lead to the erroneous estimation of transmission reliability. To overcome the above shortcomings, we propose a more realistic transmission reliability model. Firstly, considering the comprehensive effects of two failure modes, we establish a new node reliability model to better characterize the node performance. Secondly, considering the channel capacity randomness, we propose an improved SNR-Capacity connection model by deriving the one-hop connectivity probability, which can better characterize the link reliability. Then, we propose a transmission reliability evaluation model, and give the corresponding exact and approximate algorithms for its solution. Finally, taking the military Internet as an example, the effectiveness of the proposed method is verified.

Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)

To meet the growing mobility needs in intra-city transportation, the concept of urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corridors</i> , that link the aerodromes, thus avoiding the use of complex routing strategies such as those of modern-day helicopters and alleviating the burden on the ground transportation system. For safety, a UAM aircraft must use air-to-ground communications to report flight plan, off-nominal events, and real-time movement to ground base stations (GBSs). A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace and create a fast, efficient, and safe transportation system. In this paper, to characterize the wireless connectivity performance for UAM, a suitable spatial model is proposed. For the considered setup, assuming that any given aircraft communicates with the closest GBS, the distribution of the distance between an arbitrarily selected GBS and its associated aircraft and the Laplace transform of the interference experienced by the GBS are derived. Using these results, the signal-to-interference ratio (SIR)-based connectivity probability is determined to capture the connectivity performance of the UAM aircraft-to-ground communication network. Then, leveraging these connectivity results, a wireless-enabled asynchronous federated learning (AFL) framework that uses a Fourier neural network is proposed to tackle the challenging problem of turbulence prediction during UAM operations. For this AFL scheme, a staleness-aware global aggregation scheme is introduced to expedite the convergence to the optimal turbulence prediction model used by UAM aircraft. Simulation results validate the theoretical derivations for the UAM wireless connectivity. The results also demonstrate that the proposed AFL framework converges to the optimal turbulence prediction model faster than the synchronous federated learning baselines and a staleness-free AFL approach. Furthermore, the results characterize the performance of wireless connectivity and convergence of the aircraft’s turbulence model under different parameter settings, offering useful UAM design guidelines.

Optimizing Cluster Head Selection in Mobile Ad Hoc Networks: A Connectivity Probability Approach Using Poisson Distribution and Residual Energy

Optimizing Cluster Head Selection in Mobile Ad Hoc Networks: A Connectivity Probability Approach Using Poisson Distribution and Residual Energy