Partition Of Nodes Research Articles

Construction of an MRI-based decision tree to differentiate autoimmune and autoinflammatory inner ear disease from chronic otitis media with sensorineural hearing loss

Autoimmune and autoinflammatory inner ear diseases (AIED/AID) are characterized by the symptom of sensorineural hearing loss (SNHL). To date, standardized diagnostic tools for AIED/AID are lacking, and clinically differentiating AIED/AID from chronic otitis media (COM) with SNHL is challenging. This retrospective study aimed to construct a magnetic resonance imaging (MRI)-based decision tree using classification and regression tree (CART) analysis to distinguish AIED/AID from COM. In total, 67 patients were enrolled between January 2004 and October 2019, comprising AIED/AID (n = 18), COM (n = 24), and control groups (n = 25). All patients underwent 3 T temporal bone MRI, including post-contrast T1-weighted images (postT1WI) and post-contrast FLAIR images (postFLAIR). Two radiologists evaluated the presence of otomastoid effusion and inner ear contrast-enhancement on MRI. A CART decision tree model was constructed using MRI features to differentiate AIED/AID from COM and control groups, and diagnostic performance was analyzed. High-intensity bilateral effusion (61.1%) and inner ear enhancement (postFLAIR, 93.8%; postT1WI, 61.1%) were the most common findings in the AIED/AID group. We constructed two CART decision tree models; the first used effusion amount as the first partitioning node and postT1WI-inner ear enhancement as the second node, whereas the second comprised two partitioning nodes with the degree of postFLAIR-enhancement of the inner ear. The first and second models enabled distinction of AIED/AID from COM with high specificity (100% and 94.3%, respectively). The amount of effusion and the degree of inner ear enhancement on MRI may facilitate the distinction between AIED/AID and COM with SNHL using decision tree models, thereby contributing to early diagnosis and intervention.

Controlling consensus in networks with symmetries

We study networks with linear dynamics where the presence of symmetries of the pair , induces a partition of the network nodes in clusters and the matrix A is not restricted to be in Laplacian form. For these networks, an invariant group consensus subspace can be defined, in which the nodes in the same cluster evolve along the same trajectory in time. We prove that the network dynamics is uncontrollable in directions orthogonal to this subspace. Under the assumption that the dynamics parallel to this subspace is controllable, we design optimal controllers that drive the group consensus dynamics towards a desired state. Then, we consider the problem of selecting additional control inputs that stabilize the group consensus subspace and obtain bounds on the minimum number of additional inputs and driver nodes needed to this end. Altogether, our results indicate that it is possible to independently design the control actions along and transverse to the group consensus subspace.

An efficient transmission algorithm for power grid data suitable for autonomous multi-robot systems

Under the power environment of Internet of things used in autonomous multi-robot systems, due to the influence of various actual environmental factors, the security protection method based on security partition and physical isolation of nodes is difficult to fully meet the information security protection requirements of the power system, and the network threats such as impersonation, eavesdropping, and dexterity on communication links are still difficult to avoid. In order to improve the security of node data transmission in autonomous multi-robot power systems, reducing network energy consumption has become the bottleneck of network application and development. In this paper, we propose an Improved Effective Clustering Algorithm (IECA) and transmission algorithm of power grid data based on autonomous multi-robot systems. The distance between nodes sending data to the next-hop node is equivalent to the energy model. The remaining energy in the cluster head node sending data in the next hop is recorded, and the remaining energy is subtracted from the equivalent energy to find a maximum value. The corresponding node of the maximum value is taken as the number of nodes According to the received node. Compared with the LEACH algorithm and GAF algorithm, this algorithm can not only extend the lifetime of nodes but also has more efficient performance.

Model-Based Identification of Alternative Bidding Zones: Applications of Clustering Algorithms with Topology Constraints

The definition of bidding zones is a relevant question for electricity markets. The bidding zones can be identified starting from information on the nodal prices and network topology, considering the operational conditions that may lead to congestion of the transmission lines. A well-designed bidding zone configuration is a key milestone for an efficient market design and a secure power system operation, being the basis for capacity allocation and congestion management processes, as acknowledged in the relevant European regulation. Alternative bidding zone configurations can be identified in a process assisted by the application of clustering methods, which use a predefined set of features, objectives and constraints to determine the partitioning of the network nodes into groups. These groups are then analysed and validated to become candidate bidding zones. The content of the manuscript can be summarized as follows: (1) A novel probabilistic multi-scenario methodology was adopted. The approach needs the analysis of features that are computed considering a set of scenarios defined from solutions in normal operation and in planned maintenance cases. The weights of the scenarios are indicated by TSOs on the basis of the expected frequency of occurrence; (2) The relevant features considered are the Locational Marginal Prices (LMPs) and the Power Transfer Distribution Factors (PTDFs); (3) An innovative computation procedure based on clustering algorithms was developed to group nodes of the transmission electrical network into bidding zones considering topological constraints. Several settings and clustering algorithms were tested in order to evaluate the robustness of the identified solutions.

The Landscape Design of Qi Cai Yunnan No.1 Commercial District

With the increasing development of the urban commercial pedestrian street, the large flow of people has brought many troubles, such as the conflict between the floating crowd and the static crowd, the crowd crowding, noise and noise in the pedestrian street, these problems can be solved by the layout of the central square of the commercial street. Through the Landscape design of the Qi Cai Yunnan No. 1 Commercial District as an example, reasonable road planning and square node partition design are carried out in order to solve the problem of crowd congestion and provide a high-quality activity and communication space for people.

Game theoretical approach for non-overlapping community detection

Graph clustering, i.e., partitioning nodes or data points into non-overlapping clusters, can be beneficial in a large varieties of computer vision and machine learning applications. However, main graph clustering schemes, such as spectral clustering, cannot be applied to a large network due to prohibitive computational complexity required. While there exist methods applicable to large networks, these methods do not offer convincing comparisons against known ground truth. For the first time, this work conducts clustering algorithm performance evaluations on large networks (consisting of one million nodes) with ground truth information. Ideas and concepts from game theory are applied towards graph clustering to formulate a new proposed algorithm, Game Theoretical Approach for Clustering (GTAC). This theoretical framework is shown to be a generalization of both the Label Propagation and Louvain methods, offering an additional means of derivation and analysis. GTAC introduces a tuning parameter which allows variable algorithm performance in accordance with application needs. Experimentation shows that these GTAC algorithms offer scalability and tunability towards big data applications.

Overlapping community detection using core label propagation algorithm and belonging functions

The community detection in complex networks has become a major field of research. Disjoint community detection deals often with getting a partition of nodes where every node belongs to only one community. However, in social networks, individuals may belong to more than one community such as in co-purchasing field, a co-authorship of scientist papers or anthropological networks. We propose in this paper a method to find overlapping communities from pre-computed disjoint communities obtained by using the core detection label propagation. The algorithm selects candidates nodes for overlapping and uses belonging functions to decide the assignment or not of a candidate node to each of its neighbours communities. we propose and experiment in this paper several belonging functions, all based on the topology of the communities. These belonging functions are either based on global measures which are the density and the clustering coefficient or on average node measures which are the betweenness and the closeness centralities. We expose then a new similarity measure between two covers regarding the overlapping nodes. The goal is to assess the similarity between two covers that overlap several communities. We finally propose a comparative analysis with the literature algorithms.

Performance improvement of extreme multi-label classification using K-way tree construction with parallel clustering algorithm

eXtreme Multi-Label Classification (XMLC) is the particular case of Multi-Label Classification, which deals with an extremely high number of labels. The main goal is to learn extreme classifier which extracts the subset of relevant labels from extremely large label space. In an extreme environment, one of the big issues is to deal with an extreme number of features, labels and instances which affects the performance of the classifier. The high dimensional feature space and label space makes existing approaches intractable in terms of data scalability, data sparsity, training and prediction cost. The appropriate input representation technique can be used to maintain the interdependency among labels and correlation between feature space and label space. The proposed approach called “K-way Tree based eXtreme Multi-Label Classifier (KTXMLC)“ works on tree based classifier to maintain correlations using feature-label input representation technique and node partitioning using a clustering algorithm. KTXMLC constructs multi-way multiple trees using a parallel clustering algorithm, which leads to fast computational cost. KTXMLC outperforms over the existing tree based classifier in terms of ranking based measures on six datasets named Delicious, Mediamill, Eurlex-4K, Wiki10-31K, AmazonCat-13K, Delicious-200K.

A bi-directional approach to comparing the modular structure of networks

Here we propose a new method to compare the modular structure of a pair of node-aligned networks. The majority of current methods, such as normalized mutual information, compare two node partitions derived from a community detection algorithm yet ignore the respective underlying network topologies. Addressing this gap, our method deploys a community detection quality function to assess the fit of each node partition with respect to the other network’s connectivity structure. Specifically, for two networks A and B, we project the node partition of B onto the connectivity structure of A. By evaluating the fit of B’s partition relative to A’s own partition on network A (using a standard quality function), we quantify how well network A describes the modular structure of B. Repeating this in the other direction, we obtain a two-dimensional distance measure, the bi-directional (BiDir) distance. The advantages of our methodology are three-fold. First, it is adaptable to a wide class of community detection algorithms that seek to optimize an objective function. Second, it takes into account the network structure, specifically the strength of the connections within and between communities, and can thus capture differences between networks with similar partitions but where one of them might have a more defined or robust community structure. Third, it can also identify cases in which dissimilar optimal partitions hide the fact that the underlying community structure of both networks is relatively similar. We illustrate our method for a variety of community detection algorithms, including multi-resolution approaches, and a range of both simulated and real world networks.

Quantifying multiple social relationships based on a multiplex stochastic block model

Online social networks have attracted great attention recently, because they make it easy to build social connections for people all over the world. However, the observed structure of an online social network is always the aggregation of multiple social relationships. Thus, it is of great importance for real-world networks to reconstruct the full network structure using limited observations. The multiplex stochastic block model is introduced to describe multiple social ties, where different layers correspond to different attributes (e.g., age and gender of users in a social network). In this letter, we aim to improve the model precision using maximum likelihood estimation, where the precision is defined by the cross entropy of parameters between the data and model. Within this framework, the layers and partitions of nodes in a multiplex network are determined by natural node annotations, and the aggregate of the multiplex network is available. Because the original multiplex network has a high degree of freedom, we add an independent functional layer to cover it, and theoretically provide the optimal block number of the added layer. Empirical results verify the effectiveness of the proposed method using four measures, i.e., error of link probability, cross entropy, area under the receiver operating characteristic curve, and Bayes factor.

Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm: memeWSN

In wireless sensor networks for the Internet of Things (WSN‐IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat‐based WSN‐IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN‐IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large‐size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN‐IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN‐IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH‐set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new‐fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state‐of‐the‐art methods like MobAC (Singh and Lohani, 2019), EPSO‐C (Pathak, 2020), and PBC‐CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime.

Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models

Network data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional information on exogenous node partitions, these sources of knowledge are typically included in the model to supervise the cluster assignment mechanism or to improve inference on edge probabilities. Although these solutions are routinely implemented, there is a lack of formal approaches to test if a given external node partition is in line with the endogenous clustering structure encoding stochastic equivalence patterns among the nodes in the network. To fill this gap, we develop a formal Bayesian testing procedure which relies on the calculation of the Bayes factor between a stochastic block model with known grouping structure defined by the exogenous node partition and an infinite relational model that allows the endogenous clustering configurations to be unknown, random and fully revealed by the block–connectivity patterns in the network. A simple Markov chain Monte Carlo method for computing the Bayes factor and quantifying uncertainty in the endogenous groups is proposed. This strategy is evaluated in simulations, and in applications studying brain networks of Alzheimer’s patients.

A specialized interior-point algorithm for huge minimum convex cost flows in bipartite networks

The computation of the Newton direction is the most time consuming step of interior-point methods. This direction was efficiently computed by a combination of Cholesky factorizations and conjugate gradients in a specialized interior-point method for block-angular structured problems. In this work we refine this algorithmic approach to solve very large instances of minimum cost flows problems in bipartite networks, for convex objective functions with diagonal Hessians (i.e., either linear, quadratic or separable nonlinear objectives). For this class of problems the specialized algorithm only required the solution of a system by conjugate gradients at each interior-point iteration, avoiding Cholesky factorizations. After analyzing the theoretical properties of the interior-point method for this kind of problems, we provide extensive computational experiments with linear and quadratic instances of up to one billion arcs (corresponding to 200 nodes and five million nodes in each subset of the node partition, respectively). For linear and quadratic instances our approach is compared with the barriers algorithms of CPLEX (both standard path-following and homogeneous-self-dual); for linear instances it is also compared with the different algorithms of the state-of-the-art network flow solver LEMON (namely: network simplex, capacity scaling, cost scaling and cycle canceling). The specialized interior-point approach significantly outperformed the other approaches in most of the linear and quadratic transportation instances tested. In particular, it always provided a solution within the time limit; and (like LEMON, and unlike CPLEX) it never exhausted the memory of the server used for the runs. For assignment problems the network algorithms in LEMON were the most efficient option.

Detecting brain network communities: Considering the role of information flow and its different temporal scales

The identification of community structure in graphs continues to attract great interest in several fields. Network neuroscience is particularly concerned with this problem considering the key roles communities play in brain processes and functionality. Most methods used for community detection in brain graphs are based on the maximization of a parameter-dependent modularity function that often obscures the physical meaning and hierarchical organization of the partitions of network nodes. In this work, we present a new method able to detect communities at different scales in a natural, unrestricted way. First, to obtain an estimation of the information flow in the network we release random walkers to freely move over it. The activity of the walkers is separated into oscillatory modes by using empirical mode decomposition. After grouping nodes by their co-occurrence at each time scale, k-modes clustering returns the desired partitions. Our algorithm was first tested on benchmark graphs with favorable performance. Next, it was applied to real and simulated anatomical and/or functional connectomes in the macaque and human brains. We found a clear hierarchical repertoire of community structures in both the anatomical and the functional networks. The observed partitions range from the evident division in two hemispheres –in which all processes are managed globally– to specialized communities seemingly shaped by physical proximity and shared function. Additionally, the spatial scales of a network's community structure (characterized by a measure we term within-communities path length) appear inversely proportional to the oscillatory modes’ average frequencies. The proportionality constant may constitute a network-specific propagation velocity for the information flow. Our results stimulate the research of hierarchical community organization in terms of temporal scales of information flow in the brain network.

STEC-IoT: A Security Tactic by Virtualizing Edge Computing on IoT

To a large extent, the deployment of edge computing (EC) can reduce the burden of the explosive growth of the Internet of Things. As a powerful hub between the Internet of Things and cloud servers, edge devices make the transmission of cloud to things no longer complicated. However, edge nodes are faced with a series of problems, such as a large number, a wide range of distribution, and complex environment, the security of EC should not be underestimated. Based on this, we propose a tactic to improve the safety of EC by virtualizing edge nodes. In detail, first of all, we propose a strategy of edge node partition, virtualize the edge nodes dealing with different types of things into various virtual networks, which are deployed between the edge nodes and the cloud server. Second, considering that different information transmission has different security requirement, we propose a security tactic based on security level measurement. Finally, through simulation experiments, we compare with the existing advanced algorithms which are committed to virtual network security, and prove that the model proposed in this article has definite progressiveness in enhancing the security of edge computing.

Provision of traffic grooming for distributed satellite cluster networks

SummaryDistributed satellite cluster (DSC), which is usually used to construct backbone networks for space information networks, can be viewed as a single satellite carrying more payload with stronger ability. To implement traffic grooming in satellite optical backbone networks, the severe rate mismatch between wavelength and a single connection request must be considered due to the scarce characteristic of on‐board wavelength resources. In this paper, we adopt a typical DSC architecture in which the process of traffic grooming can be successfully partitioned into two independent parts, including intracluster traffic grooming and ring traffic grooming. For intracluster traffic grooming, we propose a multimode grooming method and design an improved ant colony algorithm (ACO) to obtain the optimum solutions. Simulation results show that the improved ACO yields better convergence performance than traditional ACO; in addition, the improved ACO has potentials in decreasing the number of wavelengths to be used. For ring traffic grooming, we develop a grooming circle (GC) construction algorithm based on bidirectional graph node partition. The simulation results indicate that the proposed algorithm outperforms greedy algorithm not only in the construction of full GCs but also in improving wavelength utilization efficiency.

Robust Structural Balance in Signed Networks Using a Multiobjective Evolutionary Algorithm

The aim of network structural balance is to find proper partitions of nodes that guarantee equilibrium in the system, which has attracted considerable attention in recent decades. Most of existing studies focus on reducing imbalanced components in complex networks without considering the tolerance of these balanced networks against attacks and failures. However, as indicated by some recent studies, the robustness of structurally balanced networks is also important in real applications, which should be emphasized in balancing processes. Currently, it remains challenging to define suitable robustness measures for signed networks, and few performance enhancement strategies have been designed. In this paper, two measures are designed to numerically evaluate the robustness of structurally balanced networks. Furthermore, the simultaneous enhancement on these two measures is modeled as a multiobjective optimization problem, and a multiobjective evolutionary algorithm, MOEA/D-RSB, is developed to successfully solve this problem. Experiments on synthetic and real-world networks demonstrate the good performance of MOEA/D-RSB in finding robust balanced candidates. In addition, the features of partitions with different robustness performances are analyzed to show the impact of different balancing strategies on network robustness. The obtained results are valuable in dealing with some problems arising in social and natural dynamics.

An Efficient Trivariate Algorithm for Tetrahedral Shepard Interpolation

In this paper we present a trivariate algorithm for fast computation of tetrahedral Shepard interpolants. Though the tetrahedral Shepard method achieves an approximation order better than classical Shepard formulas, it requires to detect suitable configurations of tetrahedra whose vertices are given by the set of data points. In doing that, we propose the use of a fast searching procedure based on the partitioning of domain and nodes in cubic blocks. This allows us to find the nearest neighbor points associated with each ball that need to be used in the 3D interpolation scheme. Numerical experiments show good performance of our interpolation algorithm.

A Layered Subtree Scheme for Multicast Communications in Large-Scale Elastic Translucent Optical Networks

To exploit the capacity introduced by elastic optical networks, efficient algorithms must be developed. Efficient modulation techniques have limited reach, so for distant destination nodes, the regeneration of a signal at a few intermediary nodes along the lightpath can effectively reduce the spectrum utilization and offset the extra cost, in terms of overall transceiver use, introduced by enabling the regeneration. In the context of multicast provisioning, although regeneration can be complex, it, in turn, due to its flexibility, further emphasizes the advantage of tree based routing over serving individual destinations. In this paper, we investigate the problem of routing, modulation level, spectrum allocation, and regenerator placement (RMSA-RP) for multicast provisioning, which, to the best of our knowledge, has not been previously addressed in the literature. Accordingly, we present a networking model through comprehensive integer linear programming, jointly enabling a routing method based on a subtree scheme as well as assigning a few nodes as regenerators of the signal. By means of an algorithm, we also propose a scalable framework to address RMSA-RP when the network is in operation. This algorithm implements a dynamic and automatic geographic partitioning of the destination nodes and then forms the corresponding subtree structures. Constraints taken into account include wavelength contiguity, wavelength continuity, and light splitting that affects the reach of the modulation techniques. Extensive simulation results show that the model can effectively support a greater number of demands without increasing transceiver use.

An Exact Ranked Linear Assignments Solution to the Symmetric Traveling Salesman Problem

In this paper, we show how Murty’s ranked linear assignment algorithm can be applied to exactly solve the symmetric Traveling Salesman Problem (TSP). To increase the Murty algorithm’s computational efficiency in solving the TSP, we develop a simple algorithm that determines whether a node that is generated in Murty’s sequential node partitioning process contains a sub-cycle of length less than n, where n is the number of cities to be visited. Each such node cannot generate a genuine solution, which must be a full n-cycle, and can thus be eliminated from further partitioning. In exactly, solving the TSP Murty’s ranking process continues, discarding all such nodes, terminating in a finite number of rankings when the first such ranked solution is encountered that is a full n-cycle. This first ranked n-cycle is the exact solution to the given TSP problem.