Unreliable Edges Research Articles

The Reliability of a Class of Two-Layer Networks with Unreliable Edges

It is well known that networks are dynamic graphs, and the topology of a network can be described by a graph. Thus, the reliability of a network under edge failure is defined as the probability that its corresponding topological graph remains connected under the condition that the edges fail with independent probabilities. In this paper, the reliability of a class of two-layer networks is considered, where each layer is a complete graph and the edges joining different layers are one-to-one correspondingly connected. The edge failure probability is uniform in the same layer and distinct in different layers and between layers. The recursive formula for the reliability (reliability polynomial) of the two-layer network is obtained, and the corresponding algorithm is also given. Furthermore, the reliability of several networks is computed by Python, which verifies the correctness of the algorithm.

Dispelling the Fake: Social Bot Detection Based on Edge Confidence Evaluation.

Social bot detection is essential for maintaining the safety and integrity of online social networks (OSNs). Graph neural networks (GNNs) have emerged as a promising solution. Mainstream GNN-based social bot detection methods learn rich user representations by recursively performing message passing along user-user interaction edges, where users are treated as nodes and their relationships as edges. However, these methods face challenges when detecting advanced bots interacting with genuine accounts. Interaction with real accounts results in the graph structure containing camouflaged and unreliable edges. These unreliable edges interfere with the differentiation between bot and human representations, and the iterative graph encoding process amplifies this unreliability. In this article, we propose a social Bot detection method based on Edge Confidence Evaluation (BECE). Our model incorporates an edge confidence evaluation module that assesses the reliability of the edges and identifies the unreliable edges. Specifically, we design features for edges based on the representation of user nodes and introduce parameterized Gaussian distributions to map the edge embeddings into a latent semantic space. We optimize these embeddings by minimizing Kullback-Leibler (KL) divergence from the standard distribution and evaluate their confidence based on edge representation. Experimental results on three real-world datasets demonstrate that BECE is effective and superior in social bot detection. Additionally, experimental results on six widely used GNN architectures demonstrate that our proposed edge confidence evaluation module can be used as a plug-in to improve detection performance.

Privacy-preserving dual interactive wasserstein generative adversarial network for cloud-based road condition monitoring in VANETs

Nowadays, a numerous fatalities have resulted from road damage. Research into road damage detection, particularly the detection and notification of hazardous road damage is essential for enhancing the traffic security. Existing road damage identification schemes often process data in the cloud, but they are unable to warn users on time on account of lengthy latency. Although newer edge computing strategies reduce this issue owing to the limited communication range of edges, the users can only get notifications about potentially harmful road damage within a small radius. Besides, untrusted nodes may misuse the sensitive information of users. Therefore, in this manuscript, a Privacy-preserving Dual Interactive Wasserstein Generative Adversarial Network is proposed for Cloud-Based Road Condition Monitoring in VANETs (PP-DIWGAN-C-RCM-VANET). Here, local models and global models are the two types of cloud detection models are considered. In which, the local models acquire knowledge from cloud-based data, whereas global models support local models' learning by combining local models. The intention of the proposed approach is “to detect the smart hazardous road condition”. The local model recognizes dangerous roads, and also categorized into 3 levels: low, middle, and high, these are based on information gathered about the state of the roads. From the road crack dataset, it is categorized into crack, normal and pothole. DIWGAN secures secrecy from unreliable clouds, but it is still significant danger of privacy leaking from unreliable edges. To ensure privacy, users must erase all data before transmitting it to the edges, here the user indicates the owner of the vehicle. Therefore, a new privacy preserving method called Fractional Discrete Meixner Moments Encryption (FDMME) is taken into account to fulfill this requirement. For real time dataset, the proposed method achieves better accuracy 27.5%, 10.32% and 16.65%, better f-score 30.93%, 11.14% and 15.3% compared with existing methods.

MuCoGraph: A multi-scale constraint enhanced pose-graph framework for MLS point cloud inconsistency correction

The position consistency of mobile laser scanning (MLS) point clouds is crucial for large-scale applications, and is normally guaranteed by the global navigation satellite system (GNSS) and high-precision inertial measurement unit (IMU) in the data acquisition. However, GNSS-denied environments such as city valleys result in significant position inconsistency for overlapping areas, and it is difficult to automatically locate these inconsistent areas. In this paper, to overcome these problems, we present MuCoGraph, which introduces multi-scale constraints to establish the correct correspondences for revisited areas, and formulates an enhanced pose graph for position inconsistency correction. The georeferenced MLS point cloud is first sliced into segments adaptively, and these segments are then abstracted as graph vertices, which satisfy local geometric consistency and rigid transformation hypotheses. Accurate revisited graph edges are then constructed hierarchically under multi-scale scenery consistency constraints. These revisited edges are initialized based on feature-based correspondence estimation and further unreliable edge pruning. Finally, through combination with virtual co-observations, correspondence-enhanced pose-graph optimization is introduced to globally redistribute the errors and obtain a high-precision point cloud. The proposed method was used to correct the MLS point cloud position inconsistency in three datasets. The average three-dimensional distance of the checkpoints was reduced from 0.362 m, 0.108 m, and 1.027 m to 0.057 m, 0.033 m, and 0.051 m for datasets I, II, and III respectively. In addition, the root-mean-square error of all three datasets was less than 0.04 m after correction. The experiments confirmed that the proposed method can automatically locate and correct the position inconsistency of MLS point clouds, showing good robustness and effectiveness.

DGRNet: A Dual-level Graph Relation Network for Video Object Detection.

Video object detection is a fundamental and important task in computer vision. One mainstay solution for this task is to aggregate features from different frames to enhance the detection on the current frame. Off-the-shelf feature aggregation paradigms for video object detection typically rely on inferring feature-to-feature (Fea2Fea) relations. However, most existing methods are unable to stably estimate Fea2Fea relations due to the appearance deterioration caused by object occlusion, motion blur or rare poses, resulting in limited detection performance. In this paper, we study Fea2Fea relations from a new perspective, and propose a novel dual-level graph relation network (DGRNet) for high-performance video object detection. Different from previous methods, our DGRNet innovatively leverages the residual graph convolutional network to simultaneously model Fea2Fea relations at two different levels including frame level and proposal level, which facilitates performing better feature aggregation in the temporal domain. To prune unreliable edge connections in the graph, we introduce a node topology affinity measure to adaptively evolve the graph structure by mining the local topological information of pairwise nodes. To the best of our knowledge, our DGRNet is the first video object detection method that leverages dual-level graph relations to guide feature aggregation. We conduct experiments on the ImageNet VID dataset and the results demonstrate the superiority of our DGRNet against state-of-the-art methods. Especially, our DGRNet achieves 85.0% mAP and 86.2% mAP with ResNet-101 and ResNeXt-101, respectively.

Large-scale road extraction from high-resolution remote sensing images based on a weakly-supervised structural and orientational consistency constraint network

Fully supervised road segmentation neural networks from remote sensing images rely on a large number of densely labeled road samples, limiting their potential in large-scale applications. In this paper, a weakly-supervised road segmentation network based on structural and orientational consistency (SOC-RoadNet) is proposed to learn road surface from open-source road maps, thus supporting high-quality large-scale road extraction. The open-source road maps can be made with scribble-labels to train the weakly-supervised SOC-RoadNet. However, these scribble-labels that represent the approximate location of road centerlines with only one-pixel width cannot provide supervision for road boundaries, leading to low road surface segmentation accuracy using scribble-supervised methods. Existing weakly-supervised road extraction networks deploy complex scribble-label propagation procedures to generate pseudo labels to train models and exploit image edge maps to refine road boundaries. Nevertheless, road edges in images are blurry due to occlusions and spectral-confusing backgrounds, limiting reliability when processing complex scenes. The proposed SOC-RoadNet is designed as a one-stage end-to-end architecture that directly learns road surface features from scribble-labels without extra label propagation procedures. Instead of using unreliable edge maps to regularize road boundaries, a structural consistency loss function is introduced to evaluate the structural similarity between road surface segmentation results and the input images for improved road boundary accuracy. Considering that roads are elongated targets with local directional consistency, orientation learning is integrated to SOC-RoadNet to improve road integrity. A road refining module was designed to aggregate the road surface features and road orientation features to further refine the road extraction results. The reference road orientation maps are generated from the scribble labels; thus, the results of orientation learning are supervised without extra input. Extensive experimental results on the public Deep Globe Road Dataset and four city-scale images confirm the effectiveness of our SOC-RoadNet in large-scale road extraction tasks.

Optimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning

A rapid-growing machine learning technique called federated edge learning has emerged to allow a massive number of edge devices (e.g. smart phones) to collaboratively train globally shared models without revealing their private raw data. This technique not only ensures good machine learning performance but also maintains data privacy of the edge devices. However, the federated edge learning still faces the following critical challenges: (i) difficulty in avoiding unreliable edge devices acting as workers for federated edge learning, and (ii) lack of efficient learning task assignment schemes among task publishers and workers. To tackle these challenges, reputation is utilized as a metric to evaluate the trustworthiness and reliability of the edge devices. A many-to-one matching model is proposed to address the task assignment problem between task publishers and reliable workers with high reputation. For stimulating reliable edge devices to join model training and enable secure reputation management, blockchain is employed to store the training records and manage reputation data in a decentralized and secure manner without the risk of a single point of failure. Numerical results show that the proposed schemes can achieve significant performance improvement in terms of reliability of federated edge learning.

Test-retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices.

Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these “functional connectomes” are reliable over time. In a large public sample of healthy participants (N = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding. By adopting the intraclass correlation coefficient as a metric, we demonstrate that only a small portion of the functional connectome is characterized by good (6–8%) to excellent (0.08–0.14%) reliability. Connectivity between prefrontal, parietal, and temporal areas is especially reliable, but also average connectivity within known networks has good reliability. In general, while unreliable edges are weak, reliable edges are not necessarily strong. Methodologically, reliability of edges varies between atlases, global signal regression decreases reliability for networks and most edges (but increases it for some), and thresholding based on connection strength reduces reliability. Focusing on the reliable portion of the connectome could help quantify brain trait-like features and investigate individual differences using functional neuroimaging.

Finding the (k,l)-core of a tree network with unreliable edges

Given a reliable tree network containing vertices where each edge has an independent operational probability, this paper presents finding a reliable subtree with at most leaves and with a diameter of at most which maximizes the expected number of nodes that are reachable from the selected subtree by operational paths. An efficient algorithm is presented for finding a reliable tree core of. Numerical example is explained to clarify the efficient algorithm.

An efficient algorithm for computing exact system and survival signatures of K-terminal network reliability

An efficient algorithm is presented for computing exact system and survival signatures of K-terminal reliability in undirected networks with unreliable edges. K-terminal reliability is defined as the probability that a subset K of the network nodes can communicate with each other. Signatures have several advantages over direct reliability calculation such as enabling certain stochastic comparisons of reliability between competing network topology designs, extremely fast repeat computation of network reliability for different edge reliabilities and computation of network reliability when failures of edges are exchangeable but not independent. Existing methods for computation of signatures for K-terminal network reliability require derivation of cut-sets or path-sets which is only feasible for small networks due to the computational expense. The new algorithm utilises binary decision diagrams, boundary set partition sets and simple array operations to efficiently compute signatures through a factorisation of the network edges. The performance and advantages of the algorithm are demonstrated through application to a set of benchmark networks and a sensor network from an underground mine.

Thresholding functional connectomes by means of mixture modeling

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution.We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.

Edge-Based Dynamic Scheduling for Belief-Propagation Decoding of LDPC and RS Codes

This paper presents two low-complexity edge-based scheduling schemes, referred to as the e-Flooding and e-Shuffled schedules, for the belief-propagation (BP) decoding of low-density parity-check and Reed–Solomon codes. The proposed schedules selectively update the edges of the code graph based on the run-time reliability of variable and check nodes. Specifically, new message update is propagated exclusively along the unreliable edges of the code graph. This reduces the decoding complexity of BP algorithm as only a partial set of message updates is computed per decoding iteration. Besides, restricting the flow of message updates may also precludes the occurrence of some short graph cycles, which helps to preserve the BP message independence at certain variable and check nodes. Using numerical simulations, it is shown that the proposed edge-based schedules reduce the BP decoding complexity by more than 90% compared with the prior-art BP schedules, while simultaneously improving the error-rate performance, at medium-to-high signal-to-noise ratio over additive white Gaussian noise channel.

Spectral Clustering with Sparse Graph Construction Based on Markov Random Walk

Spectral clustering has become one of the most popular clustering approaches in recent years. Similarity graph constructed on the data is one of the key factors that influence the performance of spectral clustering. However, the similarity graphs constructed by existing methods usually contain some unreliable edges. To construct reliable similarity graph for spectral clustering, an efficient method based on Markov random walk (MRW) is proposed in this paper. In the proposed method, theMRW model is defined on the raw k-NN graph and the neighbors of each sample are determined by the probability of the MRW. Since the high order transition probabilities carry complex relationships among data, the neighbors in the graph determined by our proposed method are more reliable than those of the existing methods. Experiments are performed on the synthetic and real-world datasets for performance evaluation and comparison. The results show that the graph obtained by our proposed method reflects the structure of the data better than those of the state-of-the-art methods and can effectively improve the performance of spectral clustering.

An Edge-Turbulence Algorithm for the 2-MRS Problem on Trees with Unreliable Edges

The sum-max 2-most reliable sources (Sum-Max 2-MRS) problem in a given unreliable network is referred to as finding a pair of nodes in the network from which the expected number of reachable nodes is maximized. This problem is #P-hard on general graphs and admits a cubic time algorithm on trees with unreliable edges. In this paper, we revisit the problem on trees and design an edge-turbulence algorithm with a quadratic time and quadratic spaces. Finally, we further develop an edge-turbulence based parallel algorithm with a lower time complexity.

Combinatorial methods in netwotk reliability analysis

In this paper, a stochastic network is an undirected graph with unreliable edges and absolutely reliable nodes. Its connectedness probability is determined by reliability preserving network reduction. The principle of this method consists in splitting the underlying deterministic graph of the stochastic network into two edge-disjoint subgraphs via a separating node set. One of the subgraphs is replaced with a simpler structured graph (replacement graph) in such a way that the interesting reliability criterion of the original stochastic network is retained. Special attention is given to the construction of suitable replacement graphs. The case of a 3-point separating node set is considered in more detail.

Evaluation of the Reliability of Repairable (s–t) Networks by Fast Simulation Method

A network with unreliable edges is considered. All failed edges are repaired. Time distribution functions of failure free operation and edges repair are supposed to be of general type. A fast simulation method producing unbiased estimates of probability of network failure (interruption of connection between two given nodes s and t) in the given time interval is developed. It is proved that under some weak conditions an estimate has a bounded relative error with the increasing reliability of edges. Numerical example illustrates the efficiency of the method proposed.

On the 2-MRS Problem in a Tree with Unreliable Edges

This paper extends the well-known most reliable source (1-MRS) problem in unreliable graphs to the 2-most reliable source (2-MRS) problem. Two kinds of reachable probability models of node pair in unreliable graphs are considered, that is, the superior probability and united probability. The 2-MRS problem aims to find a node pair in the graph from which the expected number of reachable nodes or the minimum reachability is maximized. It has many important applications in large-scale unreliable computer or communication networks. The #P-hardness of the 2-MRS problem in general graphs follows directly from that of the 1-MRS problem. This paper deals with four models of the 2-MRS problem in unreliable trees where every edge has an independent working probability and devises a cubic-time and quadratic-space dynamic programming algorithm, respectively, for each model.

Speeding up computation of the reliability polynomial coefficients for a random graph

We consider the problem of computing the coefficients of the reliability polynomial (RP) for a random graph with reliable vertices and unreliable edges. To speed up the computation, we use the meaning of RP coefficients in one of its representations and prove vector relations over vectors of binomial coefficients.

Separating Stresses Thermoelastically in a Central Circularly Perforated Plate Using an Airy Stress Function

Abstract: Thermoelastic stress analysis (TSA) is a contemporary full‐field, non‐contacting method of experimental stress analysis. In a cyclically loaded structure which experiences adiabatic and reversible conditions, the measured local change in temperature is proportional to the change in stress. Under isotropy, the technique measures information on the sum of the principal stresses. As engineering analyses often necessitate knowing the individual components of stress, additional experimental methods or information are frequently required to ‘separate the stresses’. The ability to evaluate individual stresses reliably in a uniaxially loaded finite plate with a central circular hole from TSA‐recorded information without supplementary experimental data is demonstrated here. Measured temperature data are combined with an Airy stress function and some limited traction‐free conditions. The present inverse technique does not presuppose knowledge of the external geometry or boundary conditions, overcomes the traditional difficulties of unreliable edge data, and reduces the number of coefficients needed by satisfying the traction‐free conditions analytically on the edge of the hole. Particular attention is paid to determining a realistic value for the needed number of Airy coefficients.

Network location of a reliable center using the most reliable route policy

This paper considers a single location on an undirected network with unreliable edges that maximizes the lowest performance level of the network service with respect to all nodes. The problem is termed the reliable 1- center problem and finds applications in telecommunication and computer networks. The users are concerned with the network capability of establishing a route to some service provider. The objective function is formally stated as either minimizing the maximum expected number of unsuccessful responses to demand requests over all nodes, named the reli-minmax problem, or maximizing the minimum expected number of successful responses to demand requests over all nodes, named the reli-maxmin problem, as sub-problems of the most general reliable 1- center problem. Solutions are presented that solve the problem in polynomial time or reduce it to the 1- center problem when it is applied on general networks using a pre-designated route policy, e.g. the most reliable route policy.